Evolutionary Algorithm based Radial Basis Function Neural Network for Function Approzimation

摘要：

This study attempts to enhance the performance of radial basis function neural network (RBFnn) using selforganizing map neural network (SOMnn). In addition, the hybrid of genetic algorithm and particle swarm optimization (HGP) algorithm is employed to train RBFnn for function approximation. The proposed SOM-HGP evolutionary algorithm combines the automatically clustering ability of SOMnn and the HGP algorithm. Experimental results for three continuous test functions show that the algorithm has the best performance than GA 21, PSO 8, HPSGO 15 for training RBFnn.

关键词： evolutionary algorithm radial basis function neural network self-organizing map neural network genetic algorithm particle swarm optimization

作者: R.J.Kuo Tung-Lai Hu Zhen-Yao Chen

作者单位: Department of Industrial Management National Taiwan University of Science and Technology,Taipei,Taiw Department of Business Management National Taipei University of Technology,Taipei,Taiwan Institute of Industrial and Business Management National Taipei University of Technology,Taipei,Taiw

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）