会议专题

A NEW ALGORITHM FOR TRAINING AN RBF NETWORK

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.

RBF network function approximation chaotic signal

SHAO-QING YANG YI XIAO HONG-WEN LIN

Department of Information and Communication Engineering, Dalian Naval Academy, Dalian 116018, China

国际会议

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

大连

英文

3040-3043

2006-08-13(万方平台首次上网日期,不代表论文的发表时间)