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Synopsis
Shows how Fuzzy Logic and Neural Networks can be intgrated into a Model Reference Control context for real-time control of multivariable systems. It provides a unified architecture which accommodates several popular learning/reasoning paradigms, including Counter Propagation Networks, Radial Basis Functions and CMAC a fuzzy context. Unified treatment of fuzzy-algorithm-based and neural network based control systems. Introduces new fuzzy-nueral controller structures. Demonstrates the feasibility of proposed approach by showing applications. Graduate students of Neural Networks, Intellegent Control and fuzzy matters in depts of Electrical Engineering, Computer Science and Maths.
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Shows how Fuzzy Logic and Neural Networks can be intgrated into a Model Reference Control context for real-time control of multivariable systems. It provides a unified architecture which accommodates several popular learning/reasoning paradigms, including Counter Propagation Networks, Radial Basis Functions and CMAC a fuzzy context. Unified treatment of fuzzy-algorithm-based and neural network based control systems. Introduces new fuzzy-nueral controller structures. Demonstrates the feasibility of proposed approach by showing applications. Graduate students of Neural Networks, Intellegent Control and fuzzy matters in depts of Electrical Engineering, Computer Science and Maths.
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Fuzzy-Neural Control: Principles, Algorithms and Applications
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