Design of the fuzzy neural network controller using back-propagation artificial immune algorithm

In this paper, the FNN-BPAI controller is proposed for the nonlinear systems. 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 AI estimator. Thirdly, the training process of the AI 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.