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Sinopsis
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.
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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.
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Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)
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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. Nº de ref. del artículo: SHUB372218
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Fuzzy Model Identification For Control (Systems And Control: Foundations And Applications)
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Fuzzy Model Identification for Control
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Gebunden. Condición: 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 . Nº de ref. del artículo: 5975729
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Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)
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Fuzzy Model Identification for Control
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Fuzzy Model Identification for Control
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: 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. Nº de ref. del artículo: 9780817642389
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Fuzzy Model Identification for Control
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Fuzzy Model Identification for Control
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Fuzzy Model Identification for Control
Publicado por
Birkhäuser Boston, Birkhäuser Boston Feb 2003, 2003
ISBN 10: 0817642382
ISBN 13: 9780817642389
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 288 pp. Englisch. Nº de ref. del artículo: 9780817642389
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Fuzzy Model Identification for Control
Publicado por
Birkhauser Boston Inc, 2003
ISBN 10: 0817642382
ISBN 13: 9780817642389
Nuevo
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Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 611. Nº de ref. del artículo: C9780817642389
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