A Nonlinear Fuzzy Neural Network Modeling Approach Using an Improved Genetic Algorithm

Abstract

Fuzzy neural networks (FNNs) are quite useful for nonlinear system identification when only the input/output information is available. A new FNN framework is first proposed by combining an AutoRegressive with exogenous input (ARX) with the nonlinear Tanh function in the Takagi-Sugeno (T-S) type fuzzy consequent part. An improved genetic algorithm is then designed to optimize the structure and parameters of the FNN simultaneously under unknown plant dynamics. The hybrid encoding/decoding, neighborhood search operator, and maintain operator are presented to optimize the input structure of the ARX plus the nonlinear function submodel, the number of the fuzzy rules, and the parameters of the membership function. Three benchmarks and a liquid level modeling problem in an industrial coke furnace are utilized to compare the performance of several typical methods. Simulation results show that the proposed method is superior in structure simplification, modeling precision, and generalization capability.