Study of predictive control model of fuzzy neural network

Abstract

In order to solve the problems of nonlinearity and large time delay in complex system, this paper combined T-S RBF fuzzy neural network control with predictive control, and proposed a fuzzy neural network prediction model model which integrates the fuzzy logic ability of fuzzy control, the powerful learning ability of neural network and the nonlinear expression ability. The method of feedback correction with self-compensation ability was applied to the online correction of the prediction model model. And the controller of T-S RBF fuzzy neural network was designed. The simulation result shows that the self-adaptive predictive controller of fuzzy neural network can build accurate prediction model model for the controlled objects, control the difference of network output and sample output in a small range, and enable actual output to properly follow model output, and it can be applied to any complex nonlinear system. Meanwhile, this model has good anti-noise performance, robustness, tracking ability and self-adaptability.