Multi-scale Least Square Wavelet Support Vector Machine

摘要：

Original Least square Wavelet Support Vector Machine (LSSVM) algorithm can not reach desired precision in multi-scale regression. To solve the problem, a multi-scale wavelet LSSVM algorithm is proposed in this paper by using a wavelet kernel. Mexican-hat wavelet function is used as the support vector kernel function, and further the Least square Wavelet Support Vector Machine(LS-WSVM)algorithm is presented. On this basis, the global optimum of the multi-scale regression modeling problem can be obtained by solving a quadratic programming problem. As a result, the regression model can effectively approximate multi-scale signals. Therefore, LS-WSVM is an efficient modeling method and worth popularization and application by computer simulation results.

关键词： Wavelet Least square support vector machine Multi-scale regression

作者: Wang Xianfang Wu Ruihong Cui Jinling

作者单位: School of Computer & Information Technology,Henan Normal University,Xinxiang 453007

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2891-2895

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）