RBF Neural Network Parameters Optimization Based on an Improved PSO Algorithm

摘要：

In order to solve the promble that some parameters of RBF neural network is difficult to determine,an improved Particle Swarm Optimization(PSO) algorithm is used in this paper to optimize network parameters.The improved PSO algorithm is proposed for the disadvantage of standard PSO that is easy to fall into local optimum.Subtractive clustering algorithm,least squares methord is used to training RBF neural network,while the improved PSO algorithm is used to optimize parameters.The simulation example results show the effectiveness of this method.

关键词： Particle Swarm Optimization algorithm RBF neural network Subtractive clustering algorithm least square method

作者: Jia He Shujie Li Chenguang Zhao

作者单位: Computer and Automation Department Hebei Polytechnic University Tangshan Hebei,China Panasonic Welding Systems (Tangshan) Co.,Ltd.Tangshan Hebei.China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 309-312

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）