An Improved Particle Swarm Optimization Algorithm for Radial Basis Function Neural Network

摘要：

An improved particle swarm optimization (IMPSO) which synthesizes the existing models of constriction factor approach (CFA PSO) is proposed. In the proposed method, an adaptive algorithm based on the search space adjustable is applied to solve the problem that conventional particle swarm optimization (PSO) algorithm easily falls into local optimal and occur premature convergence. Then, the IMPSO is used to optimize the parameters of RBF neural network. The new training algorithm is used to approximate polynomial function and predict chaotic time series, compared with PSO, and CFA PSO, the algorithm speed up the speed of convergence, and has much greater accuracy.

关键词： particle swarm optimization radial basis function neural network nearest neighbor cluster algorithm constriction factor

作者: Duan Qichang Zhao Min Duan Pan

作者单位: College of Automation, Chong qing University, Chong qing, 400044 College of Electrical Engineering, Chong qing University, Chong qing, 400044

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2309-2313

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）