RBF Neural Network Parameters Optimization based on Paddy Field Algorithm

摘要：

With regard to the issue of selecting Radial Basis Functions (RBF)neural network center parameters,this paper has introduced the paddy field algorithm (PFA)for its optimization.PFA had stronger global search capacity and higher convergence speed so as to better optimize RBF neural network.In the simulation experiment,this method was applied to approximation and prediction of a typical nonlinear function and compare with PSO (Particle Swarm Optimization)algorithm and the methodology of training by traditional gradient descent algorithm.The experiment showed that all predicted errors were lower than that of PSO predicted results.

作者: Sheng Wang Dawei Dai Huijuan Hu Yen-Lun Chen Xinyu Wu

作者单位: Shenzhen Institutes of Advanced Technology,Chinese Academy Sciences,Shenzhen,China Department of Mec Shenzhen Institutes of Advanced Technology,Chinese Academy Sciences,Shenzhen,China Department of Mec School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Ch

会议类型: 国际会议

会议名称: 第八届IEEE信息与自动化国际会议(ICIC 2011)

会议地点: 深圳

会议语种:英文

页码: 349-353

在线出版日期: 2011-06-06（万方平台首次上网日期，不代表论文的发表时间）