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Synopsis
Illustrating how fuzzy logic and neural networks can be integrated into a model reference control context for real-time control of multivariable systems, this book provides a unified architecture which accommodates several popular learning/reasoning paradigms, including counter propagation networks, radial basis functions and CMAC within a fuzzy context. The book introduces new fuzzy-neural controller structures, and demonstrates the feasibility of the proposed approach by showing applications. It is designed for graduate students of neural networks, intelligent control and fuzzy matters in departments of electrical engineering, computer science and maths.
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Fuzzy-Neural Control: Principles, Algorithms and Applications
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