Research and application of extension theory-based radial basis function neural network

During the construction process of radical basis function(RBF) neural network, the structure and parameters are hard to be determined. Therefore, combining with the extension theory, an extension theory-based RBF(ERBF) neural network is proposed, in which the matter-element models including input samples and center vectors of the basis function are established, the clustering method of extension neural network type 2(ENN2) is introduced, and the hidden layer nodes number and center vectors of the basis function are dynamically adjusted by using extension analysis and extension transformation according to the sample distribution. Meanwhile, UCI standard data sets are tested, and application object is validated. Through the verification and comparison, the proposed ERBF algorithm has the advantages of simple calculation and fast convergence, which significantly enhances the generalization accuracy, robustness and stability.