多尺度最小二乘小波支持向量机的回归建模

Regression Modeling of Multi-scale Least Square Wavelet Support Vector Machine

普通最小二乘支持向量机算法用于多尺度回归建模时精度较低。针对该问题,选取墨西哥草帽小波函数作为最小二乘支持向量机的核函数,设计一种基于小波核的多尺度最小二乘小波支持向量机。在此基础上,通过解二次优化问题求出多尺度回归建模问题的全局最优解,最终得出的多尺度回归模型能够有效地逼近多尺度信号。仿真结果表明,该算法具有较高的精度。

Original Least Square 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 by using a wavelet kernel.Mexican-hat wavelet function is used as the support vector kernel function,and 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.Simulation results show that LS-WSVM is an efficient modeling method,and has high precision.