Gaussian mixture approximation algorithm based on radius basis function neural network

A algorithm based on radius basis function (RBF) neural network is presented, in which any nonlinear function can be approximated as a limited Gauss function mixture, on the basis of analysing the structure of RBF neural network. The Gauss function is selected as a radius basis function in the proposed algrithom, and the network parameters to have been trained are drawn and are used to build a mixture function. The results of theoretical analysis and simulation verify that the proposed algorithm is independent of initial values and is convergent rapidly compared with the traditional EM (expectation maximum) algorithm.