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A dynamic weight-fuzzy neural network for nonlinear dynamic system control

A class of fuzzy neural network with dynamic weights is proposed and its corresponding network topological architecture with suitable supervised learning algorithm is presented. Using the proposed network in control system design, a priori knowledge of the control system is not essential and this includes the order of the control system. The proposed network is applied to the control of a highly nonlinear pH-neutralization process. Simulation shows that the proposed dynamic learning control strategy has better dynamic quality, stronger robustness, adaptability and intelligence while comparing to the conventional control techniques, which demand the explicit and quantitative mathematical model of the system under control.