| Summary: | 碩士 === 大同大學 === 電機工程學系(所) === 99 === A fuzzy neural network (FNN) identifier based on back-propagation artificial immune (BPIA) algorithm, named the FNN-BPIA controller, is proposed for the nonlinear systems in this thesis. The proposed controller is composed of an FNN identifier, an IA estimator, a hitting controller, and a computation controller. Firstly, The FNN identifier is utilized to estimate the dynamics of the nonlinear system. These parameters which include weights, means, and standard deviations of the FNN identifier are adjusted by the BP algorithm. Secondly, the initial values which include weights, means, and standard deviations of the FNN identifier and the parameters of the BP algorithm are estimated by the IA estimator. Thirdly, the training process of the IA estimator has four stages which include initialization, crossover, mutation, and evolution. Further, the computation controller is given to calculate the control effect and the hitting controller is utilized to eliminate the uncertainties. Finally, the inverted pendulum system and the second-order chaotic system are simulated to verify the performance and the effectiveness of the FNN-BPIA controller.
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