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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Bibliographic Details

Title: Fuzzy Model Identification for Control
Publisher: Birkh�user Boston
Publication Date: 2003
Language: English
ISBN 10: 0817642382
ISBN 13: 9780817642389
Binding: Buch
Condition: Neu
Dimensions: 241 millimeters width by 160 millimeters height by 21 millimeters depth
Weight: 606 grams

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Synopsis

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.

Synopsis

Three years ago a new approach was developed for the design of fuzzy controllers. Instead of attempting to model the operator's decision making process, this new design strategy uses a fuzzy model of the process itself and imbeds this in a model-based control. This book presents a new framework developed for fuzzy model-based control and recent advances in fuzzy identification and control. In order to provide a deeper understanding, the main features of the techniques are illustrated by means of several simulated examples and real-world applications taken from chemical and process engineering practice. Features include: three chapters on applied fuzzy model and its control-relevant properties providing the reader with the basics; a detailed review of algorithms and approaches developed for modeling and identification for control; simulated examples on the book's website by means of MATLAB and Simulink program code; and extensive references to give an overview of the current state of identification and control of dynamic systems and fuzzy modeling.

Advanced undergraduate and graduate students (electrical, control, process and chemical engineering) interested in intelligent control and researchers in the field of fuzzy systems will find this book a useful and practical resource.

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