Prediction of uniaxial compressive strength of rock samples using density weighted least squares twin support vector regression

2021 ◽  
Author(s):  
Deepak Gupta ◽  
N. Natarajan
Keyword(s):  
Compressive Strength ◽  
Least Squares ◽  
Support Vector Regression ◽  
Uniaxial Compressive Strength ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Rock Samples

A sparse method for least squares twin support vector regression

2016 ◽  
Vol 211 ◽  
pp. 150-158 ◽  
Author(s):  
Huajuan Huang ◽  
Xiuxi Wei ◽  
Yongquan Zhou
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Twin Support Vector Regression

Primal least squares twin support vector regression

2013 ◽  
Vol 14 (9) ◽  
pp. 722-732 ◽  
Author(s):  
Hua-juan Huang ◽  
Shi-fei Ding ◽  
Zhong-zhi Shi
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Twin Support Vector Regression

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.

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