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Engineering - Control Engineering | System Identification and Adaptive Control - Theory and Applications of the Neurofuzzy and Fuzzy

System Identification and Adaptive Control

Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models

Boutalis, Y., Theodoridis, D., Kottas, T., Christodoulou, M.A.

2014, XII, 313 p. 120 illus., 56 illus. in color.

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  • 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

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.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Content Level » Research

Keywords » Adaptive Control - Adaptive Estimation - Fuzzy Cognitive Maps - Fuzzy Cognitive Networks - Neurofuzzy Models

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - Control Engineering - Production & Process Engineering

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