Structural parameter optimization of radial basis function neural network based on improved genetic algorithm and cost function model

Abstract

This paper investigates the structural parameter optimization of RBF networks with the goal of economic control. The cost function and its implementation method are analyzed, and the cost function model of RBF neural network is established. The weights of RBF neural network are determined using recursive least squares method, then the crossover and mutation operators of genetic algorithm are improved and a new adaptive genetic algorithm is designed to implement the economic control of RBF neural network. The optimized network structure parameters are applied to the RBF neural network for simulation through function example, and the results obtained are compared with those of ordinary RBF network training. It shows that the method proposed in this paper is superior in both error and prediction values.