Study of RBF Neural Network Based on Improved OLS Algorithm

Abstract

Training of parameters in RBF neural network, this article proposes an optimization of RBF neural network parameters algorithm, which can overcome the disadvantages of select of data center and weights in RBF neural network. The algorithm process input data normalization and compute network output and hidden layer output angle cosine firstly, a set of data being established as the network center when cosine value is most minimum. And then determine network weights based on OLS algorithm. Simulation results show that the algorithm can reduce the training sample data and increase network training speed when train RBF neural network.