A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

2015 ◽  
Vol 38 (2) ◽  
pp. 122-129 ◽  
Author(s):  
Mohammad-Ali Ahmadi ◽  
Mahdi Zeinali Hasanvand ◽  
Alireza Bahadori
Keyword(s):  
Support Vector Machine ◽  
Pressure Drop ◽  
Natural Gas ◽  
Least Squares ◽  
Temperature Drop ◽  
Gas Production ◽  
Support Vector ◽  
Natural Gas Production

Modeling of stability conditions of natural gas clathrate hydrates using least squares support vector machine approach

2016 ◽  
Vol 223 ◽  
pp. 1081-1092 ◽  
Author(s):  
Mohammad M. Ghiasi ◽  
Hamidreza Yarveicy ◽  
Milad Arabloo ◽  
Amir H. Mohammadi ◽  
Reza M. Behbahani
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
Least Squares ◽  
Clathrate Hydrates ◽  
Support Vector ◽  
Stability Conditions

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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