Authors:
- Summarizes the latest studies in neurofuzzy control
- Explains how to apply two powerful models in a variety of systems
- Provides the reader with mutually reinforcing rigorous theoretical proof and simulation
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Industrial Control (AIC)
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Table of contents (10 chapters)
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Front Matter
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The Recurrent Neurofuzzy Model
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Front Matter
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Back Matter
About this book
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in:
• contemporary power generation;
• process control and
• conventional benchmarking problems.
Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.
Authors and Affiliations
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Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas
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Kifisia, Greece
Manolis A. Christodoulou
About the authors
Bibliographic Information
Book Title: System Identification and Adaptive Control
Book Subtitle: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models
Authors: Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/978-3-319-06364-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-06363-8Published: 08 May 2014
Softcover ISBN: 978-3-319-35412-5Published: 03 September 2016
eBook ISBN: 978-3-319-06364-5Published: 23 April 2014
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XII, 313
Number of Illustrations: 64 b/w illustrations, 56 illustrations in colour
Topics: Control and Systems Theory, Artificial Intelligence, Computational Intelligence, Industrial and Production Engineering