A Genetic Designed Beta Basis Function Neural Network for Approximating Multi-Variables Functions

摘要

We propose in this paper a new genetic algorithm for Beta basis function neural networks (BBFNN). The proprieties of this genetic algorithm are the representation used and the ability to obtain the optimal structure of the BBFNN for approximating a multi-variable function. Each network is coded as a matrix for which the number of rows is equal to the number of parameters in the function. The genetic algorithm operators change the number of neurons in the hidden layer. Some applications to functions with one and two variables are considered to demonstrate the performance of the BBFNN and of their genetic algorithm based design.