SIMULATION OF FUZZY NEURAL NETWORK ALGORITHM IN DYNAMIC NONLINEAR SYSTEM

Abstract

The identification of nonlinear system is studied to establish an accurate model of coordination system. The original dynamic fuzzy neural network (DFNN) is first used to identify the nonlinear system for finding the existing problems. Aiming at the problems found, two improvements are made. For the problem of too many pre-set parameters in the original algorithm, the fuzzy completeness [Formula: see text] is introduced to allocate the parameters, and the width of the membership function is modified. The simulation results reveal that the fuzzy neural network (FNN) model not improved produces seven fuzzy rules, and the root mean square error (RMSE) of training is 0.0261, while the improved FNN model produces six fuzzy rules, and the RMSE of training is 0.0161. The improved network has more advantages in performance. The model proposed provides some references for the application of FNN in dynamic nonlinear systems.