A performance nondestructive pruning method for reduced robust weighted Least Squares Support Vector Regression

2016 ◽  
Author(s):  
Zhang Guo ◽  
GangQuan Si ◽  
JianQuan Shi ◽  
YiWei Yuan ◽  
YanBin Zhang
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Pruning Method ◽  
A Performance

The Quality Prediction in Small-Batch Producing Process Based on Weighted Least Squares Support Vector Regression

2012 ◽  
Vol 542-543 ◽  
pp. 411-415
Author(s):  
Chao Yong Yan ◽  
Yao Jun Yu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Time Window ◽  
Learning Algorithm ◽  
Weighted Least Squares ◽  
Prediction Method ◽  
Machining Process ◽  
Support Vector ◽  
Quality Prediction ◽  
Outer Race

A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

scholarly journals Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Liu ◽  
Aijia Ouyang ◽  
Zhonghua Yun
Keyword(s):  
Nonlinear System ◽  
Least Squares ◽  
Support Vector Regression ◽  
Data Reduction ◽  
System Modeling ◽  
Weighted Least Squares ◽  
Computational Time ◽  
Support Vector ◽  
Nonlinear System Modeling ◽  
Overlap Factor

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).

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