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 modelstems 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."
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