Approximating Multimodal Functions Using Stochastic Search Method

摘要：

The radial basis function (RBF) is well known dynamic recursion neural network. However, RBF weights and thresholds, which are trained by back propagation algorithm, the gradient descent method and genetic algorithm, will be fixed after the training completing. The adaptive ability is bad. To improve RBF identification performance, particle swarm optimization (PSO), which is a stochastic search algorithm, is employed to train and adjust RBF structure parameter online. The simulation experiments show that PSONN has less adjustable parameters, faster convergence speed and higher precision in multimodal functions identification.

关键词： multimodal functions dynamic identification radial basis function particle swarm optimizatio

作者: Jian Guo Hongmin Li

作者单位: Wuhan Polytechnic UniversityWuhan, China Wuhan Polytechnic University Wuhan, China

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information Management and Engineering(2010年IEEE第二届信息管理与工程国际会议 IEEE ICIME 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-04-16（万方平台首次上网日期，不代表论文的发表时间）