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Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)
Seller: JERO BOOKS AND TEMPLET CO., SANTA MONICA, CA, U.S.A.
Seller rating 5 out of 5 stars
First Edition
Hardcover. Condition: Very Good. Dust Jacket Condition: No DJ. First Edition. First Edition. Hardcover without Dust Jacket. 8vo with 271 pages. The book is in very good condition with slight shelf wear such as bumping to edges. Interior is clean and tight, No markings or hi-lighting. Black spine with white text. No online access or CD-ROM or digital access codes if applicable! Please check the ISBN# assigned to this book to be certain that it is the same ISBN# of the book that is needed!! Read the description there will be a 20% restocking fee if returned and shipping will not be refunded or paid. Size: 8vo. Hardcover.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
Paperback. Condition: Brand New. 64 pages. 8.66x5.91x0.15 inches. In Stock.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing Feb 2016, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
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.
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Condition: New.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
Taschenbuch. Condition: Neu. Fuzzy Model Identification & Control of Non-Linear Systems | Sunil Gupta (u. a.) | Taschenbuch | 64 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659851148 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Fuzzy Model Identification for Control
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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PF. Condition: New.
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Condition: New.
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Fuzzy Model Identification for Control
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New. pp. 288.
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Paperback. Condition: Brand New. 284 pages. 9.25x6.10x0.65 inches. In Stock.
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Taschenbuch. Condition: Neu. Fuzzy Model Identification for Control | Janos Abonyi | Taschenbuch | xi | Englisch | 2012 | Birkhäuser Boston | EAN 9781461265795 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Hardcover. Condition: Brand New. 256 pages. 9.50x6.50x0.75 inches. In Stock.
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Condition: New. pp. 288 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.
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Condition: New. pp. 288.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.
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Fuzzy Model Identification for Control
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: As New. Unread book in perfect condition.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing Feb 2016, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
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.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
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.
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Fuzzy Model Identification & Control of Non-Linear Systems
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659851140 ISBN 13: 9783659851148
Language: English
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.
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Fuzzy Model Identification for Control
Published by Birkhäuser Boston Okt 2012, 2012
ISBN 10: 1461265797 ISBN 13: 9781461265795
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms. 288 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files .
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files .
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Fuzzy Model Identification for Control
Published by Birkhauser Boston Inc, 2003
ISBN 10: 0817642382 ISBN 13: 9780817642389
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 611.
