scholarly journals Hybrid Support Vector Regression and Genetic Algorithm Model for Tuning Magnetic Ordering Temperature of Manganite Refrigerant

2019 ◽  
Vol 13 (1) ◽  
pp. 87-93
Author(s):  
Abdullah Alqahtani ◽  
Taoreed O. Owolabi ◽  
Kabiru O. Akanded ◽  
Sunday O. Olatunji ◽  
Nahier Aldhafferi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Magnetic Ordering ◽  
Support Vector ◽  
Magnetic Ordering Temperature ◽  
Genetic Algorithm Model

Support Vector Regression Ensemble for Effective Modeling of Magnetic Ordering Temperature of Doped Manganite in Magnetic Refrigeration

2019 ◽  
Vol 195 (1-2) ◽  
pp. 179-201 ◽  
Author(s):  
Taoreed O. Owolabi ◽  
Kabiru O. Akande ◽  
Sunday O. Olatunji ◽  
Nahier Aldhafferi ◽  
Abdullah Alqahtani
Keyword(s):  
Support Vector Regression ◽  
Magnetic Ordering ◽  
Magnetic Refrigeration ◽  
Support Vector ◽  
Magnetic Ordering Temperature

scholarly journals Applying hybrid support vector regression and genetic algorithm to water alternating CO 2 gas EOR

2020 ◽  
Vol 10 (3) ◽  
pp. 613-630 ◽  
Author(s):  
Menad Nait Amar ◽  
Noureddine Zeraibi ◽  
Ashkan Jahanbani Ghahfarokhi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Support Vector

Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model

2012 ◽  
Vol 468-471 ◽  
pp. 579-582
Author(s):  
Wei Sun ◽  
Le Shen
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Empirical Analysis ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
High Accuracy ◽  
Support Vector ◽  
Type Selection

Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.

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