A new sequential learning algorithm for RBF neural networks  

作　　者：YANG Ge1, LV Jianhong1 & LIU Zhiyuan2 1. Department of Power Engineering, Southeast University, Nanjing 210096, China 2. Department of Power Engineering, Nanjing Institute of Technology, Nanjing 210013, China 

出　　处：《Science China(Technological Sciences)》2004年第4期447-460,共14页中国科学（技术科学英文版）

基　　金：the National Natural Science Foundation of China (Grant No.50076008).

摘　　要：Due to their inherent imperfections, it is hard to use the static neural networks for nonlinear time-varying process modeling and prediction, and the minimal resource allocation network (MRAN) is difficult to be realized for its too many regulation parameters. A new sequential learning algorithm for radial basis function (RBF) neural networks based on local projection named Local Projection Network (LPN) is proposed in this paper. The results of validation for several benchmark problems with the new algorithm show that the presented LPN not only has the same level as M-RAN in network size and precision of the outputs, but also has fewer regulation parameters and is more predictable.Due to their inherent imperfections, it is hard to use the static neural networks for nonlinear time-varying process modeling and prediction, and the minimal resource allocation network (MRAN) is difficult to be realized for its too many regulation parameters. A new sequential learning algorithm for radial basis function (RBF) neural networks based on local projection named Local Projection Network (LPN) is proposed in this paper. The results of validation for several benchmark problems with the new algorithm show that the presented LPN not only has the same level as M-RAN in network size and precision of the outputs, but also has fewer regulation parameters and is more predictable.

关 键 词：RADIAL basis functions  local PROJECTION network  MINIMAL resource ALLOCATION network  learning algorithm. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]