Interval analysis using least squares support vector fuzzy regression

被引：0
|
作者：
Yongqi Chen
Qijun Chen
机构：
[1] [1,Chen, Yongqi
[2] Chen, Qijun
来源：
Chen, Y. (chenyongqi@nbu.edu.cn) | 1600年 / South China University of Technology卷 / 10期
关键词：
26;
D O I：
10.1007/s11768-012-9205-z
中图分类号：
学科分类号：
摘要：
A least squares support vector fuzzy regression model (LS-SVFR) is proposed to estimate uncertain and imprecise data by applying the fuzzy set principle to weight vectors. This model only requires a set of linear equations to obtain the weight vector and the bias term, which is different from the solution of a complicated quadratic programming problem in existing support vector fuzzy regression models. Besides, the proposed LS-SVFR is a model-free method in which the underlying model function doesn’t need to be predefined. Numerical examples and fault detection application are applied to demonstrate the effectiveness and applicability of the proposed model.
引用
收藏
页码：458 / 464
页数：6
相关论文
共 50 条
  • [1] Interval analysis using least squares support vector fuzzy regression
    Yongqi CHEN
    Qijun CHEN
    Journal of Control Theory and Applications, 2012, 10 (04) : 458 - 464
  • [2] Least Squares Support Vector Fuzzy Regression
    Chen Yongqi
    2012 INTERNATIONAL CONFERENCE ON FUTURE ELECTRICAL POWER AND ENERGY SYSTEM, PT A, 2012, 17 : 711 - 716
  • [3] Intuitionistic fuzzy C-regression by using least squares support vector regression
    Lin, Kuo-Ping
    Chang, Hao-Feng
    Chen, Tung-Lian
    Lu, Yu-Ming
    Wang, Ching-Hsin
    EXPERT SYSTEMS WITH APPLICATIONS, 2016, 64 : 296 - 304
  • [4] Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising
    Shuqiong Xu
    Zhi Liu
    Yun Zhang
    Soft Computing, 2016, 20 : 1459 - 1470
  • [5] Feasible generalized least squares using support vector regression
    Miller, Steve
    Startz, Richard
    ECONOMICS LETTERS, 2019, 175 : 28 - 31
  • [6] Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising
    Xu, Shuqiong
    Liu, Zhi
    Zhang, Yun
    SOFT COMPUTING, 2016, 20 (04) : 1459 - 1470
  • [7] An improved support vector regression using least squares method
    Cheng Yan
    Xiuli Shen
    Fushui Guo
    Structural and Multidisciplinary Optimization, 2018, 57 : 2431 - 2445
  • [8] An improved support vector regression using least squares method
    Yan, Cheng
    Shen, Xiuli
    Guo, Fushui
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2018, 57 (06) : 2431 - 2445
  • [9] Least squares Support Vector Machine regression for discriminant analysis
    Van Gestel, T
    Suykens, JAK
    De Brabanter, J
    De Moor, B
    Vandewalle, J
    IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 2445 - 2450
  • [10] Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
    Lin, Kuo-Ping
    Pai, Ping-Feng
    Lu, Yu-Ming
    Chang, Ping-Teng
    INFORMATION SCIENCES, 2013, 220 : 196 - 209
12345