Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator

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Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator</title> <meta name="description" content="Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator"> <meta name="keywords" content="Artificial neural network, back propagation, inverse kinematics, manipulator, robot."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator"> <meta name="citation_author" content="Thiang"> <meta name="citation_author" content="Handry Khoswanto"> <meta name="citation_author" content="Rendy Pangaldus"> <meta name="citation_publication_date" content="2009/12/29"> <meta name="citation_journal_title" content="International Journal of 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Inverse Kinematics of Manipulator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Thiang">Thiang</a>, <a href="https://publications.waset.org/search?q=Handry%20Khoswanto"> Handry Khoswanto</a>, <a href="https://publications.waset.org/search?q=Rendy%20Pangaldus"> Rendy Pangaldus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.</p> <iframe src="https://publications.waset.org/541.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20network" title="Artificial neural network">Artificial neural network</a>, <a href="https://publications.waset.org/search?q=back%20propagation" title=" back propagation"> back propagation</a>, <a href="https://publications.waset.org/search?q=inverse%20kinematics" title=" inverse kinematics"> inverse kinematics</a>, <a href="https://publications.waset.org/search?q=manipulator" title=" manipulator"> manipulator</a>, <a href="https://publications.waset.org/search?q=robot." title=" robot."> robot.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1328618" target="_blank">doi.org/10.5281/zenodo.1328618</a> </p> <a href="https://publications.waset.org/541/artificial-neural-network-with-steepest-descent-backpropagation-training-algorithm-for-modeling-inverse-kinematics-of-manipulator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/541/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/541/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/541/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/541/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/541/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/541/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/541/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/541/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/541/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/541/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/541.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">2288</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Dan W. Patterson. Artificial Neural Networks, Theory and Applications. Singapore: Prentice Hall, 1996. <br>[2] Thiang, Rianto Chandra, Iwan Njoto Sandjaja, "One Arm Robot Position Control Using Artificial Neural Network," in Proceedings of National Seminar: The Application of Technology Toward Better Life. Yogyakarta, 2005. <br>[3] Thiang, Indar Sugiarto, Hendrik Chandra, "DC Motor Speed Control System Using Artificial Neural Network," in Proceedings of National Seminar of Computer Science and Information Technology, Jakarta, 2004. <br>[4] Felix Pasila, "Multivariate Inputs for Electrical Load Forecasting on Hybrid Neuro-Fuzzy and Fuzzy C-Means Forecaster," in Proceedings of International Conference on Fuzzy Systems, Hongkong, 2008. <br>[5] Felix Pasila, Sautma Ronni, Thiang, Lie Hendra Wijaya, "Long-term Forecasting in Financial Stock Market using Accelerated LMA on Neuro-Fuzzy Structure and Additional Fuzzy C-Means Clustering for Optimizing the GMFs," in Proceedings of International Joint Conference on Neural Networks. Hongkong, 2008. <br>[6] Felix Pasila, "Neuro-Fuzzy Forecaster for Modeling and Forecasting Electrical Load Competition Using Multivariate Inputs on Takagi- Sugeno Networks," in Proceedings of International Conference on Soft Computing, Intelligent System, and Information Technology. Bali, 2007. <br>[7] A.M.S., Zalzala and Morris, A.S., Neural Network For Robotic Control, Theory And Application. London: Ellis Horwood, 1996. <br>[8] Craig, John J., Introduction to Robotics: Mechanics and Control. New Jersey: Prentice Hall, 2005. <br>[9] 9. Martin T. Hagan, Neural Network Design. Boston: PWS Publishing, 1996. <br>[10] Zurada, J.M., Introduction To Artificial Neural Systems. 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Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator