Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)

Abonyi, Janos

ISBN 10: 0817642382 / ISBN 13: 9780817642389

Published by Birkhäuser, 2003

About this Item

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.

Seller Inventory # 030651

Bibliographic Details

• Title: Fuzzy Model Identification for Control (Systems & Control Foundations & Applicat)
• Publisher: Birkhäuser
• Publication Date: 2003
• Language: English
• ISBN 10: 0817642382
• ISBN 13: 9780817642389
• Binding: Hardcover
• Edition: First Edition.
• Condition: Very Good
• Dust Jacket Condition: No DJ
• Book Type: Hardcover

About this title

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.