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Fuzzy Model Identification & Control of Non-Linear Systems - Softcover

 
9783659851148: Fuzzy Model Identification & Control of Non-Linear Systems

Synopsis

This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input –output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator’s control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox.

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About the Author

Mr.Sunil Gupta is working as a Reader in Maharaja Surajmal Institute of Technology,(GGSIP University), Janak Puri, New Delhi. He is pursuing Ph.D. degree in Electrical Engineering at Jamia Millia Islamia (A Central University), New Delhi, India. His research interest includes IEC 61850 based utility automation and smart grid.

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ISBN 10: 3659851140 ISBN 13: 9783659851148
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input -output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator's control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox. 64 pp. Englisch. Seller Inventory # 9783659851148

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Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta SunilMr.Sunil Gupta is working as a Reader in Maharaja Surajmal Institute of Technology,(GGSIP University), Janak Puri, New Delhi. He is pursuing Ph.D. degree in Electrical Engineering at Jamia Millia Islamia (A Central Univers. Seller Inventory # 158606010

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Tushir, Meena/ Gupta, Sunil
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
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Paperback. Condition: Brand New. 64 pages. 8.66x5.91x0.15 inches. In Stock. Seller Inventory # 3659851140

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Taschenbuch. Condition: Neu. Neuware -This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input ¿output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator¿s control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Seller Inventory # 9783659851148

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Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input -output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator's control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox. Seller Inventory # 9783659851148

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