A Growing Algorithm for RBF Neural Network

This paper presents a growing algorithm to design the architecture of RBF neural network called growing RBF neural network algorithm (GRBF). The GRBF starts from a single prototype randomly initialized in the feature space; the whole algorithm consists of two major parts: the structure learning phase and parameter adjusting phase. In the structure algorithm, the growing strategy is used to judge when and where the RBF neural network should be grown in the hidden layer based on the sensitivity analysis of the network output. In the parameter adjusting strategy, the whole weights of the RBF should be adjusted for improving the whole capabilities of the GRBF. In the end, the proposed GRBF network is employed to track non-linear functions. The computational complexity analysis and the results of the simulations confirm the efficiency of the proposed algorithm.