Overview
- For the first time in monograph form, the reader can learn the use of fuzzy models in predictive control
- The reader will learn a novel approach to the construction of fuzzy models which compounds the data from real situations with the consideration of linguistic integrity which is increasingly beneficial in dealing with the vagaries of real-life control
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Industrial Control (AIC)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose.
Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining.
This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Reviews
From the reviews:
"New insights into the transfer of fuzzy methods into the modern control paradigms encompassing robust, model-based, PID-like, and predictive control are presented in this book. … Five appendices support the already extensive results of the chapters by proofs, explanations and illustrative examples. The book (263 pages, 138 figures, 95 references) is of interest to researchers in the field of data mining, artificial intelligence, modeling, and control. Also, the realistic examples provide good material to graduate students and engineers." (Ingmar Randvee, Zentralblatt MATH, Vol. 1061 (12), 2005)
Authors and Affiliations
About the authors
Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book.
Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.
Bibliographic Information
Book Title: Fuzzy Logic, Identification and Predictive Control
Authors: Jairo Espinosa, Joos Vandewalle, Vincent Wertz
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/b138626
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London 2005
Hardcover ISBN: 978-1-85233-828-2Published: 03 December 2004
Softcover ISBN: 978-1-84996-931-4Published: 21 October 2010
eBook ISBN: 978-1-84628-087-0Published: 04 January 2007
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XX, 264
Topics: Electrical Engineering, Information Storage and Retrieval, Artificial Intelligence, Control and Systems Theory, Complexity