Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling
Joint Authors
Ouyang, Aijia
Liu, Xiaoyong
Yun, Zhonghua
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-10
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This paper proposes a fuzzy weighted least squares support vector regression (FW-LSSVR) with data reduction for nonlinear system modeling based only on the measured data.
The proposed method combines the advantages of data reduction with some ideas of fuzzy weighted mechanism.
It not only possesses the capability of illuminating local characteristic of the modeled plant but also can deal with the problem of boundary effects resulted from local LSSVR method when the modeled data is at the boundary of whole data subset.
Furthermore, in comparison of the SVR, the proposed method only utilizes fewer hyperparameters to construct model, and the overlap factor λ can be chosen in relatively smaller value than SVR to further reduce more computational time.
First of all, distilling the original input space into several regions with fuzzy partition by applying Gustafson-Kessel clustering algorithm (GKCA) is a foundation for data reduction and the overlap factor is introduced to reduce the size of subsets.
Following that, those subset regression models (SRMs) which can be simultaneously solved by LSSVR are integrated into an overall output of the estimated nonlinear system by fuzzy weighted.
Finally, the proposed method is demonstrated by experimental analysis and compared with local LSSVR, weighted SVR, and global LSSVR methods by using the index of computational time and root-mean-square error (RMSE).
American Psychological Association (APA)
Liu, Xiaoyong& Ouyang, Aijia& Yun, Zhonghua. 2018. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
Modern Language Association (MLA)
Liu, Xiaoyong…[et al.]. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
American Medical Association (AMA)
Liu, Xiaoyong& Ouyang, Aijia& Yun, Zhonghua. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208844