Pattern classification with a PSO optimization based elliptical basis function neural networks

Ji-Xiang Du, De-Shuang Huang, Zengfu Wang

Abstract: In this paper, a novel model of elliptical basis function neural networks (EBFNN) based on a hybrid optimization algorithm is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. And the shape parameters of kernel function for the hidden layer are also optimized by the PSO simultaneously. Finally, the hybrid learning algorithm (HLA) is further applied to adjust the hidden centers and the shape parameters of kernel function for the hidden layer. The experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.