A NEW ALGORITHM FOR TRAINING AN RBF NETWORK

摘要：

Artificial neural networks (ANNs) are important tools for function estimation. However, the existing ANNs for fitting functions at least have one of the following drawbacks: 1) low accuracy 2) unstable training process 3) long learning time 4)too many hidden nodes. In this paper, based on the orthogonal least squares learning algorithm, a new approach is proposed which uses the gradient descent method to optimally determine the spread of RBFs for training an RBF network.The experimental results show the new method overcomes the above disadvantages even ff fitting a chaotic signal.

关键词： RBF network function approximation chaotic signal

作者: SHAO-QING YANG YI XIAO HONG-WEN LIN

作者单位: Department of Information and Communication Engineering, Dalian Naval Academy, Dalian 116018, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3040-3043

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）