关键词: 稳健, 代理模型, 加权最小二乘支持向量机, 异常样本

Abstract: Surrogate model based on Least Squares Support Vector Machine（LS-SVM）has preferable generalization ability and powerful non-linear expression ability, but LS-SVM is very sensitive to outliers which are inevitable in actual projects. Traditional Weighted Least Squares Support Vector Machine（WLS-SVM）often has the problem of over fitting, and it does not consider the regression error distribution characteristic. Aiming at these problems, normal distribution probability density function weighted method is presented, and the median value of regression error is selected as criteria for computing weighted value in order to improve the weighted algorithm robustness. Moreover, fast recursive algorithm for iteratively weighted LS-SVM is proposed. Matrix relation is utilized in this fast algorithm for iteratively recursive calculation, which can reduce computation and save modeling time. Lastly, the results of numerical regression experiment validate the feasibility and effectiveness of this method.

Key words: robust, surrogate model, Weighted Least Squares Support Vector Machine（WLS-SVM）, outliers