A Self-Organizing Fuzzy Neural Network for Identification and Control of Nonlinear Systems

In this paper we designed a self-organizing fuzzy neural network (SOFNN) by structure learning and parameter learning. A hybrid learning algorithm, by combining back propagation and recursive least-squares (RLS) algorithm with forgetting factor, was used to learn the optimal parameters of the SOFNN. Furthermore, the fuzzy system was constructed and evaluated under the Schwarz & Rissanen information criterion (SRIC). Finally, several simulation examples of identification and model-reference tracking control of nonlinear systems were presented and analyzed to demonstrate the effectiveness of the proposed method, and the effect of the threshold parameter in fuzzy rule learning algorithm was also discussed.