scholarly journals Comparing support vector machines with Gaussian kernels to radial basis function classifiers

1997 ◽  
Vol 45 (11) ◽  
pp. 2758-2765 ◽  
Author(s):  
B. Scholkopf ◽  
Kah-Kay Sung ◽  
C.J.C. Burges ◽  
F. Girosi ◽  
P. Niyogi ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Gaussian Kernels ◽  
Vector Machines ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines

Energy ◽  
2021 ◽  
pp. 122064
Author(s):  
Tiago de Oliveira Nogueira ◽  
Gilderlânio Barbosa Alves Palacio ◽  
Fabrício Damasceno Braga ◽  
Pedro Paulo Nunes Maia ◽  
Elineudo Pinho de Moura ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Radial Basis Function ◽  
Wind Turbine ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Vector Machines ◽  
Radial Basis

Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines

2016 ◽  
Vol 819 ◽  
pp. 569-574 ◽  
Author(s):  
A.A. Ibrahim ◽  
T.A. Lemma ◽  
Moey Lip Kean ◽  
Mesfin Gizaw Zewge
Keyword(s):  
Support Vector Machines ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Hydrate Formation ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Gas Hydrate Formation ◽  
Vector Machines ◽  
Radial Basis

The oil and gas industry struggles to prevent formation of hydrates in pipeline by spending substantial amount of dollars. Hydrates are ice-like crystalline compounds that are composed of water and gas in which the gas molecules are trapped in water cavities. The hydrate formation is favorable at elevated pressure and reduced temperature and can be determined through experiment. However, the cost involved to determine early hydrate formation using experiment is driving researchers to seek for robust prediction methods using statistical and analytical methods. Main aim of the present study is to investigate applicability of radial basis function networks and support vector machines to hydrate formation conditions prediction. The data needed for modeling was taken from well-established literature. Performance of the models was assessed based on MSE, MAE, MAPE, MSPE, and Modified Pearson’s Correlation Coefficient. Data-based models enable the oil industry to predict the conditions leading to hydrate formation hence preventing clogging of the pipeline and high pressure buildup that could lead to sudden burst at the connections.

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