基于滚动窗法最小二乘支持向量机的稳健预测模型<sup>*</sup>

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Abstract In this paper, the mathematical model of weighted least squares support vector machine (WLSSVM) is introduced. Based on the algorithms of heuristic learning and sliding window, a mathematical model of robust prediction of least squares support vector machine (LSSVM) using sliding window is proposed. with the modified heuristic learning algorithm, the strategy of iterative computing matrix inverse is employed to reduce the predicted time without loss of accuracy. Finally, two examples have proved that the proposed model can eliminate the outliers, realize robust prediction and achieve good results.

Key words： Weighted Least Squares Support Vector Machine (WLSSVM) Sliding Window Robustness Outlier

Received: 05 March 2007

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	ZHAO YongPing
	SUN JianGuo

Cite this article:

ZHAO YongPing,SUN JianGuo. Robust Prediction Model of Least Squares Support Vector Machine Based on Sliding Window[J]. , 2008, 21(1): 1-5.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I1/1

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