Study of RBF Neural Network Based on Improved OLS Algorithm

摘要：

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.

关键词： OLS Algorithm RBF Neural Network Adaptive Learning Cosine Method

作者: Zeng Zhezhao Jiang Jie

作者单位: College of Electric & Information Engineering Changsha University of Science &Technology Changsha 410077, Hunan, P. R. China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 244-247

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）