RADIAL BASIS PROBABILISTIC NEURAL NETWORKS OF GENETIC OPTIMIZATION OF FULL STRUCTURE
全结构遗传优化径向基概率神经网络

The genetic algorithm was used to optimize the full structure radial basis probabilistic neural networks(RBPNN), including selecting the hidden centers vectors of the first hidden layer and determining the matching controlling parameters of kernel function of RBPNN. The proposed genetic encoding method not only completely embodies the space distribution characterizes of pattern samples, but also simultaneously achieves the optimum number of the selected hidden centers vectors and the matching controlling parameters of the kernel function. The novelly constructed fitting function can efficiently control the error accuracy of the RBPNN output. The experimental results show that the algorithm effectivelfies simpliy the structure of PBPNN.