An Improved LSSVM Based on Multi-scale Wavelet Kernel Function
This work presents a methodology for the problem of the original Least Square Support Vector Machine (LSSVM) algorithm could not reach desired precision in multi-scale regression. In this methodology, we choose Mexican hat wavelet function as the kernel function of LSSVM firstly, and then the global optimum of the multi-scale regression modeling problem can be obtained by solving a quadratic programming problem. The regression model can effectively approximate multi-scale signals. The effectiveness of the proposed algorithm is validated by computer simulation results.
Least square support vector machine Wavelet Multi-scale regression
DU Zhiyong WANG Xianfang
Henan Mechanical and Electrical Engineering College, Xinxiang, P.R. China 453002 School of Computer and Information Technology, Henan Normal University, Xinxiang, P.R. China 453007
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
7095-7100
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）