Conjugate gradient-based Takagi-Sugeno fuzzy neural network parameter identification and its convergence analysis

Abstract

Model identification is divided into two parts: structure identification and parameter identification, and the parameter identification is actually an optimization process. For improving the optimization performance, in this paper, we firstly present a novel conjugate gradient descent method with a modified Armijo-type line search technique to train a Takagi-Sugeno fuzzy neural network model. Numerical simulations are implemented to demonstrate the efficiency of the proposed algorithm. According to the experimental comparisons that are evaluated over 15 classification and 3 regression problems, the advantages of the given method are superior to its another two counterparts. To complement the simulation results and help in establishing a robust fuzzy neural network model, we strictly prove two deterministic convergent behaviors of the presented algorithm, i.e., weak and strong convergence results. They indicate the gradient of the target function with respect to network weights converges to zero and the parameter sequence approaches a fixed optimal point, respectively.