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In this thesis, we present a design method for a model reference control structure using fuzzy neural networks (FNN). A simple fuzzy logic based neural networks system is first studied. Knowledge of rules is explicitly encoded in the weights of the proposed fuzzy neural networks and inferences are executed efficiently high rate. Two proposed fuzzy neural networks are utilized in the proposed model reference control structure. One is a controller, called the Fuzzy Neural Networks Controller (FNNC); the other is an identifier, called the Fuzzy Neural Networks Identifier (FNNI). The control action issued by the FNNC is updated by observing the controlled results through the FNNI. Adaptive learning rates for both the FNNC and FNNI are guaranteed to converge by a Lyapunov function. We compare the proposed fuzzy neural networks with the Horikawa's type I FNN in the simulation. The on-line control ability, robustness, learning ability, and interpolation ability of the proposed model reference control structure using fuzzy neural networks are confirmed by simulation results.
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