Least Squares Support Vector Fuzzy Regression

被引：1
|
作者：
Chen Yongqi [1 ]
机构：
[1] Ningbo Univ, Coll Sci & Technol, Ningbo 315211, Zhejiang, Peoples R China
来源：
2012 INTERNATIONAL CONFERENCE ON FUTURE ELECTRICAL POWER AND ENERGY SYSTEM, PT A | 2012年 / 17卷
关键词：
Interval analysis; least squares; fuzzy regression; fuzzy sets; outliers;
D O I：
10.1016/j.egypro.2012.02.160
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A least squares support vector fuzzy regression model (LS_SVFR) is proposed to estimate uncertain and imprecise data by applying the fuzzy sets principle in weight vector. Determining the weight vector and the bias term of this model requires only a set of linear equations, as against the solution of a complicated quadratic programming problem in existing support vector fuzzy regression model. Numerical example is given to demonstrate the effectiveness and applicability of the proposed model. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Hainan University.
引用
收藏
页码：711 / 716
页数：6
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