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style="font-size:1.6rem;">Search results for: adaptive neuro-fuzzy inference system (ANFIS)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18382</span> Prediction of Compressive Strength in Geopolymer Composites by Adaptive Neuro Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrzad%20Mohabbi%20Yadollahi">Mehrzad Mohabbi Yadollahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Demirbo%C4%9Fa"> Ramazan Demirboğa</a>, <a href="https://publications.waset.org/abstracts/search?q=Majid%20Atashafrazeh"> Majid Atashafrazeh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Geopolymers are highly complex materials which involve many variables which makes modeling its properties very difficult. There is no systematic approach in mix design for Geopolymers. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength an ANFIS (Adaptive neuro fuzzy inference system) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of ANFIS for predicting the compressive strength has been studied. Consequently, ANFIS can be used for geopolymer compressive strength prediction with acceptable accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geopolymer" title="geopolymer">geopolymer</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20strength" title=" compressive strength"> compressive strength</a>, <a href="https://publications.waset.org/abstracts/search?q=mix%20design" title=" mix design"> mix design</a> </p> <a href="https://publications.waset.org/abstracts/16977/prediction-of-compressive-strength-in-geopolymer-composites-by-adaptive-neuro-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">853</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18381</span> Home Legacy Device Output Estimation Using Temperature and Humidity Information by Adaptive Neural Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sung%20Hyun%20Yoo">Sung Hyun Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=In%20Hwan%20Choi"> In Hwan Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ho%20Jung"> Jun Ho Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Ki%20Ahn"> Choon Ki Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Myo%20Taeg%20Lim"> Myo Taeg Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20fuzzy%20inference%20system%20%28ANFIS%29" title="artificial neural fuzzy inference system (ANFIS)">artificial neural fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20energy%20management%20system%20%28HEMS%29" title=" home energy management system (HEMS)"> home energy management system (HEMS)</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20device" title=" smart device"> smart device</a>, <a href="https://publications.waset.org/abstracts/search?q=legacy%20device" title=" legacy device"> legacy device</a> </p> <a href="https://publications.waset.org/abstracts/42360/home-legacy-device-output-estimation-using-temperature-and-humidity-information-by-adaptive-neural-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42360.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">543</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18380</span> A Comparative Study on ANN, ANFIS and SVM Methods for Computing Resonant Frequency of A-Shaped Compact Microstrip Antennas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Kayabasi">Ahmet Kayabasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akdagli"> Ali Akdagli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=a-shaped%20compact%20microstrip%20antenna" title="a-shaped compact microstrip antenna">a-shaped compact microstrip antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN)"> artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system%20%28ANFIS%29" title=" adaptive neuro-fuzzy inference system (ANFIS)"> adaptive neuro-fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/31100/a-comparative-study-on-ann-anfis-and-svm-methods-for-computing-resonant-frequency-of-a-shaped-compact-microstrip-antennas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31100.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18379</span> Improvement of Transient Voltage Response Using PSS-SVC Coordination Based on ANFIS-Algorithm in a Three-Bus Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I%20Made%20Ginarsa">I Made Ginarsa</a>, <a href="https://publications.waset.org/abstracts/search?q=Agung%20Budi%20Muljono"> Agung Budi Muljono</a>, <a href="https://publications.waset.org/abstracts/search?q=I%20Made%20Ari%20Nrartha"> I Made Ari Nrartha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transient voltage response appears in power system operation when an additional loading is forced to load bus of power systems. In this research, improvement of transient voltage response is done by using power system stabilizer-static var compensator (PSS-SVC) based on adaptive neuro-fuzzy inference system (ANFIS)-algorithm. The main function of the PSS is to add damping component to damp rotor oscillation through automatic voltage regulator (AVR) and excitation system. Learning process of the ANFIS is done by using off-line method where data learning that is used to train the ANFIS model are obtained by simulating the PSS-SVC conventional. The ANFIS model uses 7 Gaussian membership functions at two inputs and 49 rules at an output. Then, the ANFIS-PSS and ANFIS-SVC models are applied to power systems. Simulation result shows that the response of transient voltage is improved with settling time at the time of 4.25 s. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=improvement" title="improvement">improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=transient%20voltage" title=" transient voltage"> transient voltage</a>, <a href="https://publications.waset.org/abstracts/search?q=PSS-SVC" title=" PSS-SVC"> PSS-SVC</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=settling%20time" title=" settling time"> settling time</a> </p> <a href="https://publications.waset.org/abstracts/4811/improvement-of-transient-voltage-response-using-pss-svc-coordination-based-on-anfis-algorithm-in-a-three-bus-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4811.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">577</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18378</span> Application of Neuro-Fuzzy Technique for Optimizing the PVC Membrane Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Rezayi">Majid Rezayi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh.%20Shahaboddin"> Sh. Shahaboddin</a>, <a href="https://publications.waset.org/abstracts/search?q=HNM%20E.%20Mahmud"> HNM E. Mahmud</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yadollah"> A. Yadollah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Saeid"> A. Saeid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yatimah"> A. Yatimah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference" title="adaptive neuro fuzzy inference">adaptive neuro fuzzy inference</a>, <a href="https://publications.waset.org/abstracts/search?q=PVC%20sensor" title=" PVC sensor"> PVC sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=titanium%20%28III%29%20ions" title=" titanium (III) ions"> titanium (III) ions</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerntian%20slope" title=" Nerntian slope"> Nerntian slope</a> </p> <a href="https://publications.waset.org/abstracts/57746/application-of-neuro-fuzzy-technique-for-optimizing-the-pvc-membrane-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57746.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18377</span> Hybrid Wavelet-Adaptive Neuro-Fuzzy Inference System Model for a Greenhouse Energy Demand Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azzedine%20Hamza">Azzedine Hamza</a>, <a href="https://publications.waset.org/abstracts/search?q=Chouaib%20Chakour"> Chouaib Chakour</a>, <a href="https://publications.waset.org/abstracts/search?q=Messaoud%20Ramdani"> Messaoud Ramdani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title="wavelet transform">wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption%20prediction" title=" energy consumption prediction"> energy consumption prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse" title=" greenhouse"> greenhouse</a> </p> <a href="https://publications.waset.org/abstracts/163632/hybrid-wavelet-adaptive-neuro-fuzzy-inference-system-model-for-a-greenhouse-energy-demand-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18376</span> An Adaptive Neuro-Fuzzy Inference System (ANFIS) Modelling of Bleeding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Abbas%20Tabatabaei">Seyed Abbas Tabatabaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Fereydoon%20Moghadas%20Nejad"> Fereydoon Moghadas Nejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Saed"> Mohammad Saed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The bleeding prediction of the asphalt is one of the most complex subjects in the pavement engineering. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used for modeling the effect of important parameters on bleeding is trained and tested with the experimental results. bleeding index based on the asphalt film thickness differential as target parameter,asphalt content, temperature depth of two centemeter, heavy traffic, dust to effective binder, Marshall strength, passing 3/4 sieves, passing 3/8 sieves,passing 3/16 sieves, passing NO8, passing NO50, passing NO100, passing NO200 as input parameters. Then, we randomly divided empirical data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 72 percent of empirical data. 28 percent of primary data which had been considered for testing the approprativity of the modeling were entered into ANFIS model. Results were compared by two statistical criterions (R2, RMSE) with empirical ones. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can also be promoted to more general states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bleeding" title="bleeding">bleeding</a>, <a href="https://publications.waset.org/abstracts/search?q=asphalt%20film%20thickness%20differential" title=" asphalt film thickness differential"> asphalt film thickness differential</a>, <a href="https://publications.waset.org/abstracts/search?q=Anfis%20Modeling" title=" Anfis Modeling"> Anfis Modeling</a> </p> <a href="https://publications.waset.org/abstracts/16218/an-adaptive-neuro-fuzzy-inference-system-anfis-modelling-of-bleeding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">269</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18375</span> Modeling of Age Hardening Process Using Adaptive Neuro-Fuzzy Inference System: Results from Aluminum Alloy A356/Cow Horn Particulate Composite</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chidozie%20C.%20Nwobi-Okoye">Chidozie C. Nwobi-Okoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Basil%20Q.%20Ochieze"> Basil Q. Ochieze</a>, <a href="https://publications.waset.org/abstracts/search?q=Stanley%20Okiy"> Stanley Okiy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research reports on the modeling of age hardening process using adaptive neuro-fuzzy inference system (ANFIS). The age hardening output (Hardness) was predicted using ANFIS. The input parameters were ageing time, temperature and percentage composition of cow horn particles (CHp%). The results show the correlation coefficient (R) of the predicted hardness values versus the measured values was of 0.9985. Subsequently, values outside the experimental data points were predicted. When the temperature was kept constant, and other input parameters were varied, the average relative error of the predicted values was 0.0931%. When the temperature was varied, and other input parameters kept constant, the average relative error of the hardness values predictions was 80%. The results show that ANFIS with coarse experimental data points for learning is not very effective in predicting process outputs in the age hardening operation of A356 alloy/CHp particulate composite. The fine experimental data requirements by ANFIS make it more expensive in modeling and optimization of age hardening operations of A356 alloy/CHp particulate composite. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system%20%28ANFIS%29" title="adaptive neuro-fuzzy inference system (ANFIS)">adaptive neuro-fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=age%20hardening" title=" age hardening"> age hardening</a>, <a href="https://publications.waset.org/abstracts/search?q=aluminum%20alloy" title=" aluminum alloy"> aluminum alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20matrix%20composite" title=" metal matrix composite"> metal matrix composite</a> </p> <a href="https://publications.waset.org/abstracts/83874/modeling-of-age-hardening-process-using-adaptive-neuro-fuzzy-inference-system-results-from-aluminum-alloy-a356cow-horn-particulate-composite" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83874.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18374</span> Application of Adaptive Neuro Fuzzy Inference Systems Technique for Modeling of Postweld Heat Treatment Process of Pressure Vessel Steel AASTM A516 Grade 70</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Al%20Denali">Omar Al Denali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Badi"> Abdelaziz Badi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of post-weld heat treatment (PWHT) experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556 %, which confirms the high accuracy of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=post-weld%20heat%20treatment" title=" post-weld heat treatment"> post-weld heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title=" adaptive neuro-fuzzy inference system"> adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20percentage%20error" title=" mean absolute percentage error"> mean absolute percentage error</a> </p> <a href="https://publications.waset.org/abstracts/148849/application-of-adaptive-neuro-fuzzy-inference-systems-technique-for-modeling-of-postweld-heat-treatment-process-of-pressure-vessel-steel-aastm-a516-grade-70" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18373</span> Application of Two Stages Adaptive Neuro-Fuzzy Inference System to Improve Dissolved Gas Analysis Interpretation Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kharisma%20Utomo%20Mulyodinoto">Kharisma Utomo Mulyodinoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Suwarno"> Suwarno</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Abu-Siada"> A. Abu-Siada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dissolved Gas Analysis is one of impressive technique to detect and predict internal fault of transformers by using gas generated by transformer oil sample. A number of methods are used to interpret the dissolved gas from transformer oil sample: Doernenberg Ratio Method, IEC (International Electrotechnical Commission) Ratio Method, and Duval Triangle Method. While the assessment of dissolved gas within transformer oil samples has been standardized over the past two decades, analysis of the results is not always straight forward as it depends on personnel expertise more than mathematical formulas. To get over this limitation, this paper is aimed at improving the interpretation of Doernenberg Ratio Method, IEC Ratio Method, and Duval Triangle Method using Two Stages Adaptive Neuro-Fuzzy Inference System (ANFIS). Dissolved gas analysis data from 520 faulty transformers was analyzed to establish the proposed ANFIS model. Results show that the developed ANFIS model is accurate and can standardize the dissolved gas interpretation process with accuracy higher than 90%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=dissolved%20gas%20analysis" title=" dissolved gas analysis"> dissolved gas analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Doernenberg%20ratio%20method" title=" Doernenberg ratio method"> Doernenberg ratio method</a>, <a href="https://publications.waset.org/abstracts/search?q=Duval%20triangular%20method" title=" Duval triangular method"> Duval triangular method</a>, <a href="https://publications.waset.org/abstracts/search?q=IEC%20ratio%20method" title=" IEC ratio method"> IEC ratio method</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/103022/application-of-two-stages-adaptive-neuro-fuzzy-inference-system-to-improve-dissolved-gas-analysis-interpretation-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18372</span> ANFIS Approach for Locating Faults in Underground Cables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdy%20B.%20Eteiba">Magdy B. Eteiba</a>, <a href="https://publications.waset.org/abstracts/search?q=Wael%20Ismael%20Wahba"> Wael Ismael Wahba</a>, <a href="https://publications.waset.org/abstracts/search?q=Shimaa%20Barakat"> Shimaa Barakat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20location" title=" fault location"> fault location</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20cable" title=" underground cable"> underground cable</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/11080/anfis-approach-for-locating-faults-in-underground-cables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11080.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18371</span> Stability Enhancement of a Large-Scale Power System Using Power System Stabilizer Based on Adaptive Neuro Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agung%20Budi%20Muljono">Agung Budi Muljono</a>, <a href="https://publications.waset.org/abstracts/search?q=I%20Made%20Ginarsa"> I Made Ginarsa</a>, <a href="https://publications.waset.org/abstracts/search?q=I%20Made%20Ari%20Nrartha"> I Made Ari Nrartha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large-scale power system (LSPS) consists of two or more sub-systems connected by inter-connecting transmission. Loading pattern on an LSPS always changes from time to time and varies depend on consumer need. The serious instability problem is appeared in an LSPS due to load fluctuation in all of the bus. Adaptive neuro-fuzzy inference system (ANFIS)-based power system stabilizer (PSS) is presented to cover the stability problem and to enhance the stability of an LSPS. The ANFIS control is presented because the ANFIS control is more effective than Mamdani fuzzy control in the computation aspect. Simulation results show that the presented PSS is able to maintain the stability by decreasing peak overshoot to the value of −2.56 × 10−5 pu for rotor speed deviation Δω2−3. The presented PSS also makes the settling time to achieve at 3.78 s on local mode oscillation. Furthermore, the presented PSS is able to improve the peak overshoot and settling time of Δω3−9 to the value of −0.868 × 10−5 pu and at the time of 3.50 s for inter-area oscillation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=large-scale" title=" large-scale"> large-scale</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system" title=" power system"> power system</a>, <a href="https://publications.waset.org/abstracts/search?q=PSS" title=" PSS"> PSS</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20enhancement" title=" stability enhancement"> stability enhancement</a> </p> <a href="https://publications.waset.org/abstracts/56457/stability-enhancement-of-a-large-scale-power-system-using-power-system-stabilizer-based-on-adaptive-neuro-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56457.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18370</span> Artificial Intelligent Methodology for Liquid Propellant Engine Design Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Naseh">Hassan Naseh</a>, <a href="https://publications.waset.org/abstracts/search?q=Javad%20Roozgard"> Javad Roozgard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper represents the methodology based on Artificial Intelligent (AI) applied to Liquid Propellant Engine (LPE) optimization. The AI methodology utilized from Adaptive neural Fuzzy Inference System (ANFIS). In this methodology, the optimum objective function means to achieve maximum performance (specific impulse). The independent design variables in ANFIS modeling are combustion chamber pressure and temperature and oxidizer to fuel ratio and output of this modeling are specific impulse that can be applied with other objective functions in LPE design optimization. To this end, the LPE’s parameter has been modeled in ANFIS methodology based on generating fuzzy inference system structure by using grid partitioning, subtractive clustering and Fuzzy C-Means (FCM) clustering for both inferences (Mamdani and Sugeno) and various types of membership functions. The final comparing optimization results shown accuracy and processing run time of the Gaussian ANFIS Methodology between all methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS%20methodology" title="ANFIS methodology">ANFIS methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligent" title=" artificial intelligent"> artificial intelligent</a>, <a href="https://publications.waset.org/abstracts/search?q=liquid%20propellant%20engine" title=" liquid propellant engine"> liquid propellant engine</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/56970/artificial-intelligent-methodology-for-liquid-propellant-engine-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">588</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18369</span> Effective Supply Chain Coordination with Hybrid Demand Forecasting Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmail%20Singh">Gurmail Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bullwhip%20effect" title="bullwhip effect">bullwhip effect</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20techniques" title=" hybrid techniques"> hybrid techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=net%20stock%20amplification" title=" net stock amplification"> net stock amplification</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20flexibility" title=" supply chain flexibility"> supply chain flexibility</a> </p> <a href="https://publications.waset.org/abstracts/94150/effective-supply-chain-coordination-with-hybrid-demand-forecasting-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18368</span> Diagnosis of the Lubrification System of a Gas Turbine Using the Adaptive Neuro-Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Mahdjoub">H. Mahdjoub</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hamaidi"> B. Hamaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Zerouali"> B. Zerouali</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rouabhia"> S. Rouabhia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20diagnosis" title="fault detection and diagnosis">fault detection and diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=lubrication%20system" title=" lubrication system"> lubrication system</a>, <a href="https://publications.waset.org/abstracts/search?q=turbine" title=" turbine"> turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=training" title=" training"> training</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/31945/diagnosis-of-the-lubrification-system-of-a-gas-turbine-using-the-adaptive-neuro-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18367</span> Optimizing Boiler Combustion System in a Petrochemical Plant Using Neuro-Fuzzy Inference System and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yul%20Y.%20Nazaruddin">Yul Y. Nazaruddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Anas%20Y.%20Widiaribowo"> Anas Y. Widiaribowo</a>, <a href="https://publications.waset.org/abstracts/search?q=Satriyo%20Nugroho"> Satriyo Nugroho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=boiler" title=" boiler"> boiler</a>, <a href="https://publications.waset.org/abstracts/search?q=combustion%20process" title=" combustion process"> combustion process</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization." title=" optimization. "> optimization. </a> </p> <a href="https://publications.waset.org/abstracts/58694/optimizing-boiler-combustion-system-in-a-petrochemical-plant-using-neuro-fuzzy-inference-system-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58694.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">253</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18366</span> Applying of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Estimation of Flood Hydrographs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Ahmad%20Dehghani">Amir Ahmad Dehghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Morteza%20Nabizadeh"> Morteza Nabizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title="adaptive neuro-fuzzy inference system">adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=flood%20hydrograph" title=" flood hydrograph"> flood hydrograph</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning%20algorithm" title=" hybrid learning algorithm"> hybrid learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahid%20Rajaee%20reservoir%20dam" title=" Shahid Rajaee reservoir dam"> Shahid Rajaee reservoir dam</a> </p> <a href="https://publications.waset.org/abstracts/13913/applying-of-an-adaptive-neuro-fuzzy-inference-system-anfis-for-estimation-of-flood-hydrographs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13913.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18365</span> Adaptive Neuro Fuzzy Inference System Model Based on Support Vector Regression for Stock Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Setianingrum">Anita Setianingrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Oki%20S.%20Jaya"> Oki S. Jaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuherman%20Rustam"> Zuherman Rustam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series" title=" fuzzy time series"> fuzzy time series</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20forecasting" title=" stock forecasting"> stock forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=SVR" title=" SVR"> SVR</a> </p> <a href="https://publications.waset.org/abstracts/62703/adaptive-neuro-fuzzy-inference-system-model-based-on-support-vector-regression-for-stock-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62703.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">246</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18364</span> Scour Depth Prediction around Bridge Piers Using Neuro-Fuzzy and Neural Network Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Bonakdari">H. Bonakdari</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Ebtehaj"> I. Ebtehaj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system%20%28ANFIS%29" title="adaptive neuro-fuzzy inference system (ANFIS)">adaptive neuro-fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN)"> artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20pier" title=" bridge pier"> bridge pier</a>, <a href="https://publications.waset.org/abstracts/search?q=scour%20depth" title=" scour depth"> scour depth</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20regression%20%28NLR%29" title=" nonlinear regression (NLR)"> nonlinear regression (NLR)</a> </p> <a href="https://publications.waset.org/abstracts/73889/scour-depth-prediction-around-bridge-piers-using-neuro-fuzzy-and-neural-network-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18363</span> Evaluation of the Self-Organizing Map and the Adaptive Neuro-Fuzzy Inference System Machine Learning Techniques for the Estimation of Crop Water Stress Index of Wheat under Varying Application of Irrigation Water Levels for Efficient Irrigation Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aschalew%20C.%20Workneh">Aschalew C. Workneh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Hari%20Prasad"> K. S. Hari Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20P.%20Ojha"> C. S. P. Ojha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title="adaptive neuro-fuzzy inference system">adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=canopy%20temperature" title=" canopy temperature"> canopy temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=crop%20water%20stress%20index" title=" crop water stress index"> crop water stress index</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat" title=" wheat"> wheat</a> </p> <a href="https://publications.waset.org/abstracts/184504/evaluation-of-the-self-organizing-map-and-the-adaptive-neuro-fuzzy-inference-system-machine-learning-techniques-for-the-estimation-of-crop-water-stress-index-of-wheat-under-varying-application-of-irrigation-water-levels-for-efficient-irrigation-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">55</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18362</span> Study of ANFIS and ARIMA Model for Weather Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandreddy%20Anand%20Babu">Bandreddy Anand Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivasa%20Rao%20Mandadi"> Srinivasa Rao Mandadi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pradeep%20Reddy"> C. Pradeep Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ramesh%20Babu"> N. Ramesh Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20surmising%20tool%20stash" title=" fuzzy surmising tool stash"> fuzzy surmising tool stash</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20forecasting" title=" weather forecasting"> weather forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/13743/study-of-anfis-and-arima-model-for-weather-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18361</span> Development of Fuzzy Logic and Neuro-Fuzzy Surface Roughness Prediction Systems Coupled with Cutting Current in Milling Operation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20C.%20Chen">Joseph C. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkata%20Mohan%20Kudapa"> Venkata Mohan Kudapa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title="surface roughness">surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=input%20current" title=" input current"> input current</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro-fuzzy" title=" neuro-fuzzy"> neuro-fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=milling%20operations" title=" milling operations"> milling operations</a> </p> <a href="https://publications.waset.org/abstracts/105245/development-of-fuzzy-logic-and-neuro-fuzzy-surface-roughness-prediction-systems-coupled-with-cutting-current-in-milling-operation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105245.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18360</span> Smart Model with the DEMATEL and ANFIS Multistage to Assess the Value of the Brand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Saremi">Hamed Saremi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the challenges in manufacturing and service companies to provide a product or service is recognized Brand to consumers in target markets. They provide most of their processes under the same capacity. But the constant threat of devastating internal and external resources to prevent a rise Brands and more companies are recognizing the stages are bankrupt. This paper has tried to identify and analyze effective indicators of brand equity and focuses on indicators and presents a model of intelligent create a model to prevent possible damage. In this study identified indicators of brand equity based on literature study and according to expert opinions, set of indicators By techniques DEMATEL Then to used Multi-Step Adaptive Neural-Fuzzy Inference system (ANFIS) to design a multi-stage intelligent system for assessment of brand equity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anfis" title="anfis">anfis</a>, <a href="https://publications.waset.org/abstracts/search?q=dematel" title=" dematel"> dematel</a>, <a href="https://publications.waset.org/abstracts/search?q=brand" title=" brand"> brand</a>, <a href="https://publications.waset.org/abstracts/search?q=cosmetic%20product" title=" cosmetic product"> cosmetic product</a>, <a href="https://publications.waset.org/abstracts/search?q=brand%20value" title=" brand value"> brand value</a> </p> <a href="https://publications.waset.org/abstracts/30244/smart-model-with-the-dematel-and-anfis-multistage-to-assess-the-value-of-the-brand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18359</span> Surface Roughness Analysis, Modelling and Prediction in Fused Deposition Modelling Additive Manufacturing Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusuf%20S.%20Dambatta">Yusuf S. Dambatta</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20A.%20D.%20Sarhan"> Ahmed A. D. Sarhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title="surface roughness">surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=fused%20deposition%20modelling%20%28FDM%29" title=" fused deposition modelling (FDM)"> fused deposition modelling (FDM)</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference%20system%20%28ANFIS%29" title=" adaptive neuro fuzzy inference system (ANFIS)"> adaptive neuro fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=orientation" title=" orientation"> orientation</a> </p> <a href="https://publications.waset.org/abstracts/55529/surface-roughness-analysis-modelling-and-prediction-in-fused-deposition-modelling-additive-manufacturing-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55529.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">460</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18358</span> Anticipation of Bending Reinforcement Based on Iranian Concrete Code Using Meta-Heuristic Tools</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Sadegh%20Naseralavi">Seyed Sadegh Naseralavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Bemani"> Najmeh Bemani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference%20system" title="adaptive neuro fuzzy inference system">adaptive neuro fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=anticipate%20method" title=" anticipate method"> anticipate method</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20design%20code" title=" concrete design code"> concrete design code</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-variable%20regression" title=" multi-variable regression"> multi-variable regression</a> </p> <a href="https://publications.waset.org/abstracts/75588/anticipation-of-bending-reinforcement-based-on-iranian-concrete-code-using-meta-heuristic-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18357</span> Development of Fault Diagnosis Technology for Power System Based on Smart Meter </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Chieh%20Yang">Chih-Chieh Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chung-Neng%20Huang"> Chung-Neng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system" title=" power system"> power system</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20meter" title=" smart meter"> smart meter</a> </p> <a href="https://publications.waset.org/abstracts/104268/development-of-fault-diagnosis-technology-for-power-system-based-on-smart-meter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104268.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18356</span> Use of Artificial Intelligence Based Models to Estimate the Use of a Spectral Band in Cognitive Radio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Rivas"> Edwin Rivas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Pedraza"> Fernando Pedraza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title=" cognitive radio"> cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20primary%20user" title=" prediction primary user"> prediction primary user</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA" title=" RNA"> RNA</a> </p> <a href="https://publications.waset.org/abstracts/63037/use-of-artificial-intelligence-based-models-to-estimate-the-use-of-a-spectral-band-in-cognitive-radio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63037.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18355</span> Create a Brand Value Assessment Model to Choosing a Cosmetic Brand in Tehran Combining DEMATEL Techniques and Multi-Stage ANFIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Saremi">Hamed Saremi</a>, <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Taghavy"> Suzan Taghavy</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Hanif%20Sanjari"> Seyed Mohammad Hanif Sanjari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Kahali"> Mostafa Kahali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the challenges in manufacturing and service companies to provide a product or service is recognized Brand to consumers in target markets. They provide most of their processes under the same capacity. But the constant threat of devastating internal and external resources to prevent a rise Brands and more companies are recognizing the stages are bankrupt. This paper has tried to identify and analyze effective indicators of brand equity and focuses on indicators and presents a model of intelligent create a model to prevent possible damage. In this study, the identified indicators of brand equity are based on literature study and according to expert opinions, set of indicators By techniques DEMATEL Then to used Multi-Step Adaptive Neural-Fuzzy Inference system (ANFIS) to design a multi-stage intelligent system for assessment of brand equity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brand" title="brand">brand</a>, <a href="https://publications.waset.org/abstracts/search?q=cosmetic%20product" title=" cosmetic product"> cosmetic product</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=DEMATEL" title=" DEMATEL"> DEMATEL</a> </p> <a href="https://publications.waset.org/abstracts/17718/create-a-brand-value-assessment-model-to-choosing-a-cosmetic-brand-in-tehran-combining-dematel-techniques-and-multi-stage-anfis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17718.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">417</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18354</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18353</span> Speed Control of Hybrid Stepper Motor by Using Adaptive Neuro-Fuzzy Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Talha%20Ali%20Khan">Talha Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an adaptive neuro-fuzzy interference system (ANFIS), which is applied to a hybrid stepper motor (HSM) to regulate its speed. The dynamic response of the HSM with the ANFIS controller is studied during the starting process and under different load disturbance. The effectiveness of the proposed controller is compared with that of the conventional PI controller. The proposed method solves the problem of nonlinearities and load changes of the HSM drives. The proposed controller ensures fast and precise dynamic response with an excellent steady state performance. Matlab/Simulink program is used for this dynamic simulation study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stepper%20motor" title="stepper motor">stepper motor</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid" title=" hybrid"> hybrid</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20control" title=" speed control"> speed control</a> </p> <a href="https://publications.waset.org/abstracts/15484/speed-control-of-hybrid-stepper-motor-by-using-adaptive-neuro-fuzzy-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">551</span> </span> </div> </div> <ul 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