An incremental learning algorithm for the hybrid RBF-BP network classifier

Abstract

This paper presents an incremental learning algorithm for the hybrid RBF-BP (ILRBF-BP) network classifier. A potential function is introduced to the training sample space in space mapping stage, and an incremental learning method for the construction of RBF hidden neurons is proposed. The proposed method can incrementally generate RBF hidden neurons and effectively estimate the center and number of RBF hidden neurons by determining the density of different regions in the training sample space. A hybrid RBF-BP network architecture is designed to train the output weights. The output of the original RBF hidden layer is processed and connected with a multilayer perceptron (MLP) network; then, a back propagation (BP) algorithm is used to update the MLP weights. The RBF hidden neurons are used for nonlinear kernel mapping and the BP network is then used for nonlinear classification, which improves classification performance further. The ILRBF-BP algorithm is compared with other algorithms in artificial data sets and UCI data sets, and the experiments demonstrate the superiority of the proposed algorithm.