Fuzzy weighted least squares support vector regression with data reduction for nonlinear system modeling
type: Item
instance of: scholarly article
title: Fuzzy weighted least squares support vector regression with data reduction for nonlinear system modeling
zbMATH Open document ID: 1428.62350
DOI: 10.1155/2018/7387650
author: Xiaoyong Liu
author: Aijia Ouyang
author: Zhonghua Yun
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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 \(\lambda\) 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).
Mathematics Subject Classification ID: 62J86
zbMATH DE Number: 7019436
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7387650
OpenAlex ID: W2903855204
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description: scientific article; zbMATH DE number 7019436