An Improved Method of Wavelet Neural Network Optimization Based on Filled Function Method
BP algorithm of neural network dont obtain global minimum sometimcs2-5,furthermore,it is possible to create many local minimum so that the optimum solution cant be found. In order to solve this question,one parameter filled function methodl is presented which can calculate value fast. We combine it with modified BFGS (Broyden-Davidon-Fletcher- Powell) to get a new algorithm for global optimization of wavelet neural network. The algorithm obtain the first local minimum by BFGS,then filled function method is used to obtain another smaller local minimum,this process is repeated for some times so that the network structure and weight value are optimized till global minimum is found. This method is used to train Shanghai stock index,then better network performance is obtained.
BP algorithm Filled function Optimization Wavelet neural network
HUANG Feng-wen JIANG Ai-ping
Research Center of Finance,Shanghai Urban Management College,Shanghai,P.R.China The Sydney Institute of Language,and Commerce,Shanghai University,Shanghai,P.R.China
国际会议
北京
英文
1694-1697
2009-10-21（万方平台首次上网日期，不代表论文的发表时间）