Automatic Selection for the Beta Basis Function Neural Networks

In this paper, we propose a differential evolution algorithm based design for the beta basis function neural network. The differential Evolution algorithm has been used in many practical cases and has demonstrated good convergences properties. The differential evolution is used to evolve the beta basis function neural networks topology. Compared with the traditional genetic algorithm, the combined approach proves goodly the difference, including the feasibility and the simplicity of implementation. In the prediction of Mackey-Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to the traditional learning algorithm for a multi-layer Perceptron network and radial-basis function network. Therefore, designing a set of BBFNN can be considered as solution of a two-optimisation problem.