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squares solution</title> <meta name="description" content="Search results for: least squares solution"> <meta name="keywords" content="least squares solution"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="least squares solution"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5973</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: least squares solution</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5973</span> Least Squares Method Identification of Corona Current-Voltage Characteristics and Electromagnetic Field in Electrostatic Precipitator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Nouri">H. Nouri</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20E.%20Achouri"> I. E. Achouri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Grimes"> A. Grimes</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ait%20Said"> H. Ait Said</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Aissou"> M. Aissou</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Zebboudj"> Y. Zebboudj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to analysis the behaviour of DC corona discharge in wire-to-plate electrostatic precipitators (ESP). Current-voltage curves are particularly analysed. Experimental results show that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method of least squares. Least squares problems that of into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. A closed-form solution (or closed form expression) is any formula that can be evaluated in a finite number of standard operations. The non-linear problem has no closed-form solution and is usually solved by iterative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrostatic%20precipitator" title="electrostatic precipitator">electrostatic precipitator</a>, <a href="https://publications.waset.org/abstracts/search?q=current-voltage%20characteristics" title=" current-voltage characteristics"> current-voltage characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20method" title=" least squares method"> least squares method</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20field" title=" electric field"> electric field</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20field" title=" magnetic field"> magnetic field</a> </p> <a href="https://publications.waset.org/abstracts/39751/least-squares-method-identification-of-corona-current-voltage-characteristics-and-electromagnetic-field-in-electrostatic-precipitator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39751.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">431</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">5972</span> Genetic Algorithm to Construct and Enumerate 4×4 Pan-Magic Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younis%20R.%20Elhaddad">Younis R. Elhaddad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Alshaari"> Mohamed A. Alshaari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time. <p class="card-text"><strong>Keywords:</strong> <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=magic%20square" title=" magic square"> magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=pan-magic%20square" title=" pan-magic square"> pan-magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20intelligence" title=" computational intelligence"> computational intelligence</a> </p> <a href="https://publications.waset.org/abstracts/2917/genetic-algorithm-to-construct-and-enumerate-44-pan-magic-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2917.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">576</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">5971</span> Least Squares Solution for Linear Quadratic Gaussian Problem with Stochastic Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sie%20Long%20Kek">Sie Long Kek</a>, <a href="https://publications.waset.org/abstracts/search?q=Wah%20June%20Leong"> Wah June Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kok%20Lay%20Teo"> Kok Lay Teo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iteration%20procedure" title="iteration procedure">iteration procedure</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20solution" title=" least squares solution"> least squares solution</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20quadratic%20Gaussian" title=" linear quadratic Gaussian"> linear quadratic Gaussian</a>, <a href="https://publications.waset.org/abstracts/search?q=output%20error" title=" output error"> output error</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20approximation" title=" stochastic approximation"> stochastic approximation</a> </p> <a href="https://publications.waset.org/abstracts/113018/least-squares-solution-for-linear-quadratic-gaussian-problem-with-stochastic-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113018.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">187</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">5970</span> Public Squares and Their Potential for Social Interactions: A Case Study of Historical Public Squares in Tehran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20Mehan">Asma Mehan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under the thrust of technological changes, population growth and vehicular traffic, Iranian historical squares have lost their significance and they are no longer the main social nodes of the society. This research focuses on how historical public squares can inspire designers to enhance social interactions among citizens in Iranian urban context. Moreover, the recent master plan of Tehran demonstrates the lack of public spaces designed for the purpose of people’s social gatherings. For filling this gap, first the current situation of 7 selected primary historical public squares in Tehran including Sabze Meydan, Arg, Topkhaneh, Baherstan, Mokhber-al-dole, Rah Ahan and Hassan Abad have been compared. Later, the influencing elements on social interactions of the public squares such as subjective factors (human relationships and memories) and objective factors (natural and built environment) have been investigated. As a conclusion, some strategies are proposed for improving social interactions in historical public squares like; holding cultural, national, athletic and religious events, defining different and new functions in public squares’ surrounding, increasing pedestrian routs, reviving the collective memory, demonstrating the historical importance of square, eliminating visual obstacles across the square, organization the natural elements of the square, appropriate pavement for social activities. Finally, it is argued that the combination of all influencing factors which are: human interactions, natural elements and built environment criteria will lead to enhance the historical public squares’ potential for social interaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=historical%20square" title="historical square">historical square</a>, <a href="https://publications.waset.org/abstracts/search?q=Iranian%20public%20square" title=" Iranian public square"> Iranian public square</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20interaction" title=" social interaction"> social interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=Tehran" title=" Tehran"> Tehran</a> </p> <a href="https://publications.waset.org/abstracts/45172/public-squares-and-their-potential-for-social-interactions-a-case-study-of-historical-public-squares-in-tehran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45172.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">405</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">5969</span> Solving SPDEs by Least Squares Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Manouzi">Hassan Manouzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present in this paper a useful strategy to solve stochastic partial differential equations (SPDEs) involving stochastic coefficients. Using the Wick-product of higher order and the Wiener-Itˆo chaos expansion, the SPDEs is reformulated as a large system of deterministic partial differential equations. To reduce the computational complexity of this system, we shall use a decomposition-coordination method. To obtain the chaos coefficients in the corresponding deterministic equations, we use a least square formulation. Once this approximation is performed, the statistics of the numerical solution can be easily evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20squares" title="least squares">least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=wick%20product" title=" wick product"> wick product</a>, <a href="https://publications.waset.org/abstracts/search?q=SPDEs" title=" SPDEs"> SPDEs</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element" title=" finite element"> finite element</a>, <a href="https://publications.waset.org/abstracts/search?q=wiener%20chaos%20expansion" title=" wiener chaos expansion"> wiener chaos expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20method" title=" gradient method"> gradient method</a> </p> <a href="https://publications.waset.org/abstracts/4074/solving-spdes-by-least-squares-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4074.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">5968</span> Online Estimation of Clutch Drag Torque in Wet Dual Clutch Transmission Based on Recursive Least Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hongkui%20Li">Hongkui Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tongli%20Lu"> Tongli Lu </a>, <a href="https://publications.waset.org/abstracts/search?q=Jianwu%20Zhang"> Jianwu Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on developing an estimation method of clutch drag torque in wet DCT. The modelling of clutch drag torque is investigated. As the main factor affecting the clutch drag torque, dynamic viscosity of oil is discussed. The paper proposes an estimation method of clutch drag torque based on recursive least squares by utilizing the dynamic equations of gear shifting synchronization process. The results demonstrate that the estimation method has good accuracy and efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clutch%20drag%20torque" title="clutch drag torque">clutch drag torque</a>, <a href="https://publications.waset.org/abstracts/search?q=wet%20DCT" title=" wet DCT"> wet DCT</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20viscosity" title=" dynamic viscosity"> dynamic viscosity</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20least%20squares" title=" recursive least squares"> recursive least squares</a> </p> <a href="https://publications.waset.org/abstracts/51881/online-estimation-of-clutch-drag-torque-in-wet-dual-clutch-transmission-based-on-recursive-least-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51881.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">318</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">5967</span> Variogram Fitting Based on the Wilcoxon Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Al-Mofleh">Hazem Al-Mofleh</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Daniels"> John Daniels</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20McKean"> Joseph McKean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-linear%20wilcoxon" title="non-linear wilcoxon">non-linear wilcoxon</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimation" title=" robust estimation"> robust estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=variogram%20estimation" title=" variogram estimation"> variogram estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=wilcoxon%20norm" title=" wilcoxon norm"> wilcoxon norm</a> </p> <a href="https://publications.waset.org/abstracts/50377/variogram-fitting-based-on-the-wilcoxon-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50377.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">458</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">5966</span> Mobile Platform’s Attitude Determination Based on Smoothed GPS Code Data and Carrier-Phase Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ramdani">Mohamed Ramdani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassen%20Abdellaoui"> Hassen Abdellaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdenour%20Boudrassen"> Abdenour Boudrassen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile platform’s attitude estimation approaches mainly based on combined positioning techniques and developed algorithms; which aim to reach a fast and accurate solution. In this work, we describe the design and the implementation of an attitude determination (AD) process, using only measurements from GPS sensors. The major issue is based on smoothed GPS code data using Hatch filter and raw carrier-phase measurements integrated into attitude algorithm based on vectors measurement using least squares (LSQ) estimation method. GPS dataset from a static experiment is used to investigate the effectiveness of the presented approach and consequently to check the accuracy of the attitude estimation algorithm. Attitude results from GPS multi-antenna over short baselines are introduced and analyzed. The 3D accuracy of estimated attitude parameters using smoothed measurements is over 0.27°. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attitude%20determination" title="attitude determination">attitude determination</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20code%20data%20smoothing" title=" GPS code data smoothing"> GPS code data smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=hatch%20filter" title=" hatch filter"> hatch filter</a>, <a href="https://publications.waset.org/abstracts/search?q=carrier-phase%20measurements" title=" carrier-phase measurements"> carrier-phase measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20attitude%20estimation" title=" least-squares attitude estimation"> least-squares attitude estimation</a> </p> <a href="https://publications.waset.org/abstracts/108141/mobile-platforms-attitude-determination-based-on-smoothed-gps-code-data-and-carrier-phase-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108141.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">155</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">5965</span> Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahcene%20Habbi">Ahcene Habbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Boudouaoui"> Yassine Boudouaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20design" title="automatic design">automatic design</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid" title=" hybrid"> hybrid</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/15603/hybrid-artificial-bee-colony-and-least-squares-method-for-rule-based-systems-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15603.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">437</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">5964</span> Thin-Layer Drying Characteristics and Modelling of Instant Coffee Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apolinar%20Picado">Apolinar Picado</a>, <a href="https://publications.waset.org/abstracts/search?q=Ronald%20Sol%C3%ADs"> Ronald Solís</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20Gamero"> Rafael Gamero</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The thin-layer drying characteristics of instant coffee solution were investigated in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (80, 100 and 120 °C) and an air velocity of 1.2 m/s. Drying experimental data obtained are fitted to six (6) thin-layer drying models using the non-linear least squares regression analysis. The acceptability of the thin-layer drying model has been based on a value of the correlation coefficient that should be close to one, and low values for root mean square error (RMSE) and chi-square (x²). According to this evaluation, the most suitable model for describing drying process of thin-layer instant coffee solution is the Page model. Further, the effective moisture diffusivity and the activation energy were computed employing the drying experimental data. The effective moisture diffusivity values varied from 1.6133 × 10⁻⁹ to 1.6224 × 10⁻⁹ m²/s over the temperature range studied and the activation energy was estimated to be 162.62 J/mol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activation%20energy" title="activation energy">activation energy</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusivity" title=" diffusivity"> diffusivity</a>, <a href="https://publications.waset.org/abstracts/search?q=instant%20coffee" title=" instant coffee"> instant coffee</a>, <a href="https://publications.waset.org/abstracts/search?q=thin-layer%20models" title=" thin-layer models"> thin-layer models</a> </p> <a href="https://publications.waset.org/abstracts/74728/thin-layer-drying-characteristics-and-modelling-of-instant-coffee-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74728.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">262</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">5963</span> Dynamic Process Monitoring of an Ammonia Synthesis Fixed-Bed Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bothinah%20Altaf">Bothinah Altaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Gary%20Montague"> Gary Montague</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaine%20B.%20Martin"> Elaine B. Martin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ammonia%20synthesis%20fixed-bed%20reactor" title="ammonia synthesis fixed-bed reactor">ammonia synthesis fixed-bed reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20partial%20least%20squares%20modeling" title=" dynamic partial least squares modeling"> dynamic partial least squares modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20partial%20least%20squares" title=" recursive partial least squares"> recursive partial least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20modeling" title=" robust modeling"> robust modeling</a> </p> <a href="https://publications.waset.org/abstracts/37994/dynamic-process-monitoring-of-an-ammonia-synthesis-fixed-bed-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37994.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">393</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">5962</span> Application of the Least Squares Method in the Adjustment of Chlorodifluoromethane (HCFC-142b) Regression Models </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20J.%20de%20Bessa%20Neto">L. J. de Bessa Neto</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20Filho"> V. S. Filho</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20V.%20Ferreira%20Nunes"> J. V. Ferreira Nunes</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20C.%20Bergamo"> G. C. Bergamo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many situations in which human activities have significant effects on the environment. Damage to the ozone layer is one of them. The objective of this work is to use the Least Squares Method, considering the linear, exponential, logarithmic, power and polynomial models of the second degree, to analyze through the coefficient of determination (R²), which model best fits the behavior of the chlorodifluoromethane (HCFC-142b) in parts per trillion between 1992 and 2018, as well as estimates of future concentrations between 5 and 10 periods, i.e. the concentration of this pollutant in the years 2023 and 2028 in each of the adjustments. A total of 809 observations of the concentration of HCFC-142b in one of the monitoring stations of gases precursors of the deterioration of the ozone layer during the period of time studied were selected and, using these data, the statistical software Excel was used for make the scatter plots of each of the adjustment models. With the development of the present study, it was observed that the logarithmic fit was the model that best fit the data set, since besides having a significant R² its adjusted curve was compatible with the natural trend curve of the phenomenon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chlorodifluoromethane%20%28HCFC-142b%29" title="chlorodifluoromethane (HCFC-142b)">chlorodifluoromethane (HCFC-142b)</a>, <a href="https://publications.waset.org/abstracts/search?q=ozone" title=" ozone"> ozone</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20method" title=" least squares method"> least squares method</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20models" title=" regression models"> regression models</a> </p> <a href="https://publications.waset.org/abstracts/104490/application-of-the-least-squares-method-in-the-adjustment-of-chlorodifluoromethane-hcfc-142b-regression-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104490.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">124</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">5961</span> Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khaled%20Abduesslam">M. Khaled Abduesslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ali"> Mohammed Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Basher%20H.%20Alsdai"> Basher H. Alsdai</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nizam%20Inayati"> Muhammad Nizam Inayati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IEEE%2039%20bus" title="IEEE 39 bus">IEEE 39 bus</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20support%20vector%20machine" title=" least squares support vector machine"> least squares support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20vector%20quantization" title=" learning vector quantization"> learning vector quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20collapse" title=" voltage collapse"> voltage collapse</a> </p> <a href="https://publications.waset.org/abstracts/11211/voltage-problem-location-classification-using-performance-of-least-squares-support-vector-machine-ls-svm-and-learning-vector-quantization-lvq" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11211.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">5960</span> Near-Infrared Hyperspectral Imaging Spectroscopy to Detect Microplastics and Pieces of Plastic in Almond Flour</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Apaza">H. Apaza</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Ch%C3%A9vez"> L. Chévez</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Loro"> H. Loro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plastic and microplastic pollution in human food chain is a big problem for human health that requires more elaborated techniques that can identify their presences in different kinds of food. Hyperspectral imaging technique is an optical technique than can detect the presence of different elements in an image and can be used to detect plastics and microplastics in a scene. To do this statistical techniques are required that need to be evaluated and compared in order to find the more efficient ones. In this work, two problems related to the presence of plastics are addressed, the first is to detect and identify pieces of plastic immersed in almond seeds, and the second problem is to detect and quantify microplastic in almond flour. To do this we make use of the analysis hyperspectral images taken in the range of 900 to 1700 nm using 4 unmixing techniques of hyperspectral imaging which are: least squares unmixing (LSU), non-negatively constrained least squares unmixing (NCLSU), fully constrained least squares unmixing (FCLSU), and scaled constrained least squares unmixing (SCLSU). NCLSU, FCLSU, SCLSU techniques manage to find the region where the plastic is found and also manage to quantify the amount of microplastic contained in the almond flour. The SCLSU technique estimated a 13.03% abundance of microplastics and 86.97% of almond flour compared to 16.66% of microplastics and 83.33% abundance of almond flour prepared for the experiment. Results show the feasibility of applying near-infrared hyperspectral image analysis for the detection of plastic contaminants in food. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food" title="food">food</a>, <a href="https://publications.waset.org/abstracts/search?q=plastic" title=" plastic"> plastic</a>, <a href="https://publications.waset.org/abstracts/search?q=microplastic" title=" microplastic"> microplastic</a>, <a href="https://publications.waset.org/abstracts/search?q=NIR%20hyperspectral%20imaging" title=" NIR hyperspectral imaging"> NIR hyperspectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=unmixing" title=" unmixing"> unmixing</a> </p> <a href="https://publications.waset.org/abstracts/132107/near-infrared-hyperspectral-imaging-spectroscopy-to-detect-microplastics-and-pieces-of-plastic-in-almond-flour" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132107.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">130</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">5959</span> Analysis of Two Methods to Estimation Stochastic Demand in the Vehicle Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Torfi">Fatemeh Torfi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least-squares linear regression approach (FLLRs) to estimate the stochastic demands in the vehicle routing problem (VRP) bearing in mind the customer's preferences order. A FLLR method is proposed in solving the VRP with stochastic demands. Approximate-distance fuzzy least-squares (ADFL) estimator ADFL estimator is applied to original data taken from a case study. The SSR values of the ADFL estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed methods can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the ADFL was realistic and efficient estimator to face the stochastic demand challenges in vehicle routing system management and solve relevant problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20least-squares" title="fuzzy least-squares">fuzzy least-squares</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=location" title=" location"> location</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20problems" title=" routing problems "> routing problems </a> </p> <a href="https://publications.waset.org/abstracts/3439/analysis-of-two-methods-to-estimation-stochastic-demand-in-the-vehicle-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3439.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">434</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">5958</span> Sparse Principal Component Analysis: A Least Squares Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Merola">Giovanni Merola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the original Principal Components Analysis (PCA) objective function. This approach differs from others because it preserves PCA's original optimality: uncorrelatedness of the components and least squares approximation of the data. To identify the best subset of non-zero loadings we propose a branch-and-bound search and an iterative elimination algorithm. This last algorithm finds sparse solutions with large loadings and can be run without specifying the cardinality of the loadings and the number of components to compute in advance. We give thorough comparisons with the existing sparse PCA methods and several examples on real datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPCA" title="SPCA">SPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=uncorrelated%20components" title=" uncorrelated components"> uncorrelated components</a>, <a href="https://publications.waset.org/abstracts/search?q=branch-and-bound" title=" branch-and-bound"> branch-and-bound</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20elimination" title=" backward elimination"> backward elimination</a> </p> <a href="https://publications.waset.org/abstracts/14630/sparse-principal-component-analysis-a-least-squares-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14630.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">381</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">5957</span> Application of the Total Least Squares Estimation Method for an Aircraft Aerodynamic Model Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaouche%20Mohamed">Zaouche Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Amini%20Mohamed"> Amini Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Foughali%20Khaled"> Foughali Khaled</a>, <a href="https://publications.waset.org/abstracts/search?q=Aitkaid%20Souhila"> Aitkaid Souhila</a>, <a href="https://publications.waset.org/abstracts/search?q=Bouchiha%20Nihad%20Sarah"> Bouchiha Nihad Sarah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aerodynamic coefficients are important in the evaluation of an aircraft performance and stability-control characteristics. These coefficients also can be used in the automatic flight control systems and mathematical model of flight simulator. The study of the aerodynamic aspect of flying systems is a reserved domain and inaccessible for the developers. Doing tests in a wind tunnel to extract aerodynamic forces and moments requires a specific and expensive means. Besides, the glaring lack of published documentation in this field of study makes the aerodynamic coefficients determination complicated. This work is devoted to the identification of an aerodynamic model, by using an aircraft in virtual simulated environment. We deal with the identification of the system, we present an environment framework based on Software In the Loop (SIL) methodology and we use Microsoft<sup>TM</sup> Flight Simulator (FS-2004) as the environment for plane simulation. We propose The Total Least Squares Estimation technique (TLSE) to identify the aerodynamic parameters, which are unknown, variable, classified and used in the expression of the piloting law. In this paper, we define each aerodynamic coefficient as the mean of its numerical values. All other variations are considered as modeling uncertainties that will be compensated by the robustness of the piloting control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aircraft%20aerodynamic%20model" title="aircraft aerodynamic model">aircraft aerodynamic model</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20least%20squares%20estimation" title=" total least squares estimation"> total least squares estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=piloting%20the%20aircraft" title=" piloting the aircraft"> piloting the aircraft</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20control" title=" robust control"> robust control</a>, <a href="https://publications.waset.org/abstracts/search?q=Microsoft%20Flight%20Simulator" title=" Microsoft Flight Simulator"> Microsoft Flight Simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=MQ-1%20predator" title=" MQ-1 predator"> MQ-1 predator</a> </p> <a href="https://publications.waset.org/abstracts/44416/application-of-the-total-least-squares-estimation-method-for-an-aircraft-aerodynamic-model-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44416.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">5956</span> QSRR Analysis of 17-Picolyl and 17-Picolinylidene Androstane Derivatives Based on Partial Least Squares and Principal Component Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovi%C4%87">Sanja Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kova%C4%8Devi%C4%87"> Strahinja Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevri%C4%87"> Lidija Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Evgenija%20Djurendi%C4%87"> Evgenija Djurendić</a>, <a href="https://publications.waset.org/abstracts/search?q=Jovana%20Ajdukovi%C4%87"> Jovana Ajduković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=androstane%20derivatives" title="androstane derivatives">androstane derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=chromatography" title=" chromatography"> chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20structure" title=" molecular structure"> molecular structure</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20regression" title=" principal component regression"> principal component regression</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20regression" title=" partial least squares regression"> partial least squares regression</a> </p> <a href="https://publications.waset.org/abstracts/38073/qsrr-analysis-of-17-picolyl-and-17-picolinylidene-androstane-derivatives-based-on-partial-least-squares-and-principal-component-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38073.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">277</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">5955</span> Interaction between Mutual Fund Performance and Portfolio Turnover</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheng-Ching%20Wu">Sheng-Ching Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the interaction between mutual fund performance and portfolio turnover. Active trading could affect fund performance, but underperforming funds could also be traded actively at the same time to perform well. Therefore, we used two-stage least squares to address with simultaneity. The results indicate that funds with higher portfolio turnovers exhibit inferior performance compared with funds having lower turnovers. Moreover, funds with poor performance exhibit higher portfolio turnover. The findings support the assumptions that active trading erodes performance, and that fund managers with poor performance attempt to trade actively to retain employment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mutual%20funds" title="mutual funds">mutual funds</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20turnover" title=" portfolio turnover"> portfolio turnover</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneity" title=" simultaneity"> simultaneity</a>, <a href="https://publications.waset.org/abstracts/search?q=two-stage%20least%20squares" title=" two-stage least squares"> two-stage least squares</a> </p> <a href="https://publications.waset.org/abstracts/8033/interaction-between-mutual-fund-performance-and-portfolio-turnover" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8033.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">442</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">5954</span> Channel Estimation for LTE Downlink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashi%20Jain">Rashi Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The LTE systems employ Orthogonal Frequency Division Multiplexing (OFDM) as the multiple access technology for the Downlink channels. For enhanced performance, accurate channel estimation is required. Various algorithms such as Least Squares (LS), Minimum Mean Square Error (MMSE) and Recursive Least Squares (RLS) can be employed for the purpose. The paper proposes channel estimation algorithm based on Kalman Filter for LTE-Downlink system. Using the frequency domain pilots, the initial channel response is obtained using the LS criterion. Then Kalman Filter is employed to track the channel variations in time-domain. To suppress the noise within a symbol, threshold processing is employed. The paper draws comparison between the LS, MMSE, RLS and Kalman filter for channel estimation. The parameters for evaluation are Bit Error Rate (BER), Mean Square Error (MSE) and run-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LTE" title="LTE">LTE</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title=" channel estimation"> channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=RLS" title=" RLS"> RLS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a> </p> <a href="https://publications.waset.org/abstracts/9169/channel-estimation-for-lte-downlink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9169.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">356</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">5953</span> Equity Risk Premiums and Risk Free Rates in Modelling and Prediction of Financial Markets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ghavami">Mohammad Ghavami</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20S.%20Dilmaghani"> Reza S. Dilmaghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20methods" title="adaptive methods">adaptive methods</a>, <a href="https://publications.waset.org/abstracts/search?q=LSE" title=" LSE"> LSE</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20of%20financial%20Markets" title=" prediction of financial Markets"> prediction of financial Markets</a> </p> <a href="https://publications.waset.org/abstracts/72693/equity-risk-premiums-and-risk-free-rates-in-modelling-and-prediction-of-financial-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72693.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">336</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">5952</span> Design of Wide-Range Variable Fractional-Delay FIR Digital Filters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jong-Jy%20Shyu">Jong-Jy Shyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Soo-Chang%20Pei"> Soo-Chang Pei</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Da%20Huang"> Yun-Da Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, design of wide-range variable fractional-delay (WR-VFD) finite impulse response (FIR) digital filters is proposed. With respect to the conventional VFD filter which is designed such that its delay is adjustable within one unit, the proposed VFD FIR filter is designed such that its delay can be tunable within a wider range. By the traces of coefficients of the fractional-delay FIR filter, it is found that the conventional method of polynomial substitution for filter coefficients no longer satisfies the design demand, and the circuits perform the sinc function (sinc converter) are added to overcome this problem. In this paper, least-squares method is adopted to design WR-VFD FIR filter. Throughout this paper, several examples will be proposed to demonstrate the effectiveness of the presented methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20filter" title="digital filter">digital filter</a>, <a href="https://publications.waset.org/abstracts/search?q=FIR%20filter" title=" FIR filter"> FIR filter</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20fractional-delay%20%28VFD%29%20filter" title=" variable fractional-delay (VFD) filter"> variable fractional-delay (VFD) filter</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20approximation" title=" least-squares approximation"> least-squares approximation</a> </p> <a href="https://publications.waset.org/abstracts/8390/design-of-wide-range-variable-fractional-delay-fir-digital-filters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8390.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">491</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">5951</span> Adaptive Online Object Tracking via Positive and Negative Models Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaomei%20Li">Shaomei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yawen%20Wang"> Yawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Gao"> Chao Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title="object tracking">object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20drift" title=" tracking drift"> tracking drift</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20analysis" title=" partial least squares analysis"> partial least squares analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20models%20matching" title=" positive and negative models matching"> positive and negative models matching</a> </p> <a href="https://publications.waset.org/abstracts/19382/adaptive-online-object-tracking-via-positive-and-negative-models-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19382.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">529</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">5950</span> Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Nejib">Hamza Nejib</a>, <a href="https://publications.waset.org/abstracts/search?q=Okba%20Taouali"> Okba Taouali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20prediction" title="online prediction">online prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=KAF" title=" KAF"> KAF</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=RKHS" title=" RKHS"> RKHS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20methods" title=" Kernel methods"> Kernel methods</a>, <a href="https://publications.waset.org/abstracts/search?q=KRLS" title=" KRLS"> KRLS</a>, <a href="https://publications.waset.org/abstracts/search?q=KLMS" title=" KLMS"> KLMS</a> </p> <a href="https://publications.waset.org/abstracts/63627/online-prediction-of-nonlinear-signal-processing-problems-based-kernel-adaptive-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63627.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">399</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">5949</span> Chemometric Estimation of Inhibitory Activity of Benzimidazole Derivatives by Linear Least Squares and Artificial Neural Networks Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanja%20O.%20Podunavac-Kuzmanovi%C4%87"> Sanja O. Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Z.%20Kova%C4%8Devi%C4%87"> Strahinja Z. Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20R.%20Jevri%C4%87"> Lidija R. Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Stela%20Joki%C4%87"> Stela Jokić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines". <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antibacterial" title="Antibacterial">Antibacterial</a>, <a href="https://publications.waset.org/abstracts/search?q=benzimidazoles" title=" benzimidazoles"> benzimidazoles</a>, <a href="https://publications.waset.org/abstracts/search?q=chemometric" title=" chemometric"> chemometric</a>, <a href="https://publications.waset.org/abstracts/search?q=QSAR." title=" QSAR."> QSAR.</a> </p> <a href="https://publications.waset.org/abstracts/32354/chemometric-estimation-of-inhibitory-activity-of-benzimidazole-derivatives-by-linear-least-squares-and-artificial-neural-networks-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32354.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">316</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">5948</span> Unit Root Tests Based On the Robust Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wararit%20Panichkitkosolkul">Wararit Panichkitkosolkul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p class="Abstract" style="text-indent:10.2pt">The unit root tests based on the robust estimator for the first-order autoregressive process are proposed and compared with the unit root tests based on the ordinary least squares (OLS) estimator. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of Type I error and powers of the unit root tests are estimated via Monte Carlo simulation. Simulation results show that all unit root tests can control the probability of Type I error for all situations. The empirical power of the unit root tests based on the robust estimator are higher than the unit root tests based on the OLS estimator.<o:p></o:p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive" title="autoregressive">autoregressive</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20least%20squares" title=" ordinary least squares"> ordinary least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20i%20error" title=" type i error"> type i error</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20of%20the%20test" title=" power of the test"> power of the test</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/3693/unit-root-tests-based-on-the-robust-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3693.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">289</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">5947</span> Effect of Solution Heat Treatment on Intergranular Corrosion Resistance of Welded Stainless Steel AISI 321</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Mahmoudi">Amir Mahmoudi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this investigation, AISI321 steel after welding by Shilded Metal Arc Welding (SMAW) was solution heat treated in various temperatures and times, and then was sensitizied. Results indicated, increasing of temperature in solution heat treatment raises the sensitization and creates the cavity structure in grain boundaries. Besides, in order to examine the effect of time on solution heat treatment, all samples were solution heat treated at different times and fixed temperature (1050°C). By increasing the time, more chrome carbides were created due to dissolution of delta ferrite phase and reproduce titanium carbides. Additionally, the best process for solution heat treatment for this steel was suggested. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stainless%20steel" title="stainless steel">stainless steel</a>, <a href="https://publications.waset.org/abstracts/search?q=solution%20heat%20treatment" title=" solution heat treatment"> solution heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=intergranular%20corrosion" title=" intergranular corrosion"> intergranular corrosion</a>, <a href="https://publications.waset.org/abstracts/search?q=DLEPR" title=" DLEPR"> DLEPR</a> </p> <a href="https://publications.waset.org/abstracts/26566/effect-of-solution-heat-treatment-on-intergranular-corrosion-resistance-of-welded-stainless-steel-aisi-321" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26566.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">521</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">5946</span> Sparse Signal Restoration Algorithm Based on Piecewise Adaptive Backtracking Orthogonal Least Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang">Linyu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahui%20Ma"> Jiahui Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang"> Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanyu%20Jiang"> Hanyu Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> the traditional greedy compressed sensing algorithm needs to know the signal sparsity when recovering the signal, but the signal sparsity in the practical application can not be obtained as a priori information, and the recovery accuracy is low, which does not meet the needs of practical application. To solve this problem, this paper puts forward Piecewise adaptive backtracking orthogonal least squares algorithm. The algorithm is divided into two stages. In the first stage, the sparsity pre-estimation strategy is adopted, which can quickly approach the real sparsity and reduce time consumption. In the second stage iteration, the correction strategy and adaptive step size are used to accurately estimate the sparsity, and the backtracking idea is introduced to improve the accuracy of signal recovery. Through experimental simulation, the algorithm can accurately recover the estimated signal with fewer iterations when the sparsity is unknown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20method" title=" least square method"> least square method</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20reconstruction" title=" adaptive reconstruction"> adaptive reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/161616/sparse-signal-restoration-algorithm-based-on-piecewise-adaptive-backtracking-orthogonal-least-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161616.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">148</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">5945</span> Nonparametric Copula Approximations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Yishan%20Zang"> Yishan Zang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copulas" title="copulas">copulas</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernstein%20polynomial%20approximation" title=" Bernstein polynomial approximation"> Bernstein polynomial approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20polynomial%20approximation" title=" least-squares polynomial approximation"> least-squares polynomial approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density%20estimation" title=" kernel density estimation"> kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20approximation" title=" density approximation"> density approximation</a> </p> <a href="https://publications.waset.org/abstracts/170324/nonparametric-copula-approximations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170324.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">74</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">5944</span> Solution of Hybrid Fuzzy Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmood%20Otadi">Mahmood Otadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Mosleh"> Maryam Mosleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hybrid differential equations have a wide range of applications in science and engineering. In this paper, the homotopy analysis method (HAM) is applied to obtain the series solution of the hybrid differential equations. Using the homotopy analysis method, it is possible to find the exact solution or an approximate solution of the problem. Comparisons are made between improved predictor-corrector method, homotopy analysis method and the exact solution. Finally, we illustrate our approach by some numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20number" title="fuzzy number">fuzzy number</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20ODE" title=" fuzzy ODE"> fuzzy ODE</a>, <a href="https://publications.waset.org/abstracts/search?q=HAM" title=" HAM"> HAM</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20method" title=" approximate method"> approximate method</a> </p> <a href="https://publications.waset.org/abstracts/31754/solution-of-hybrid-fuzzy-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31754.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">511</span> </span> </div> </div> <ul class="pagination"> <li 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