The optimal design of neural fuzzy controller

Neural fuzzy controllers have the advantages of ease for knowledge expression and the ability of self-learning and are able to learn to control adaptively by updating the fuzzy rules and the membership functions. Nevertheless, the long training time usually discourages their applications in industry and the over-tuned may cause system oscillate extensively. In this paper, a method for optimizing neural fuzzy controller is proposed. The only that of parameter which affect the control performance significantly are updated and updating step is adjusted adaptively in accordance with the error and the change of error of the system.