Distance relaying for transmission line using support vector machine and radial basis function neural network

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2007.01.007 ◽

2007 ◽

Vol 29 (7) ◽

pp. 551-556 ◽

Author(s):

S.R. Samantaray ◽

P.K. Dash ◽

G. Panda

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Transmission Line ◽

Radial Basis Function ◽

Basis Function ◽

Support Vector ◽

Radial Basis ◽

Distance Relaying

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Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models

Journal of Water Supply Research and Technology—AQUA ◽

10.2166/aqua.2016.069 ◽

2016 ◽

Vol 66 (1) ◽

pp. 15-24 ◽

Author(s):

Siyu Nie ◽

Jianmin Bian ◽

Hanli Wan ◽

Xiaoqing Sun ◽

Bingjing Zhang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Uncertainty Analysis ◽

Radial Basis Function ◽

Basis Function ◽

Support Vector ◽

Groundwater Levels ◽

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Combining support vector machine with radial basis function kernel and information gain for sentiment analysis of movie reviews

Journal of Physics Conference Series ◽

10.1088/1742-6596/1918/4/042157 ◽

2021 ◽

Vol 1918 (4) ◽

pp. 042157

Author(s):

Z Abidin ◽

W Destian ◽

R Umer

Keyword(s):

Support Vector Machine ◽

Radial Basis Function ◽

Sentiment Analysis ◽

Basis Function ◽

Information Gain ◽

Support Vector ◽

Radial Basis Function Kernel ◽

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Binary glioma grading framework employing locality preserving projections and Gaussian radial basis function support vector machine

International Journal of Imaging Systems and Technology ◽

10.1002/ima.22615 ◽

2021 ◽

Author(s):

Rahul Singh ◽

Aditya Goel ◽

Deepak Kumar Raghuvanshi

Keyword(s):

Support Vector Machine ◽

Radial Basis Function ◽

Basis Function ◽

Support Vector ◽

Locality Preserving Projections ◽

Locality Preserving ◽

Glioma Grading ◽

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A fast and accurate distance relaying scheme using an efficient radial basis function neural network

Electric Power Systems Research ◽

10.1016/s0378-7796(01)00150-x ◽

2001 ◽

Vol 60 (1) ◽

pp. 1-8 ◽

Author(s):

A.K. Pradhan ◽

P.K. Dash ◽

G. Panda

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis ◽

Distance Relaying

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Random Radial Basis Function Kernel-based Support Vector Machine

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2021.10.005 ◽

2021 ◽

Author(s):

Xiaojian Ding ◽

Jian Liu ◽

Fan Yang ◽

Jie Cao

Keyword(s):

Support Vector Machine ◽

Radial Basis Function ◽

Basis Function ◽

Support Vector ◽

Radial Basis Function Kernel ◽

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Petrographic image classification using optimized Radial Basis Function Support Vector Machine & validation of its asymptotic behavior

2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) ◽

10.1109/icspcc.2016.7753703 ◽

2016 ◽

Author(s):

Ashutosh S. Marathe ◽

Vibha Vyas ◽

Madhavi Chavhan

Keyword(s):

Support Vector Machine ◽

Asymptotic Behavior ◽

Radial Basis Function ◽

Image Classification ◽

Basis Function ◽

Support Vector ◽

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Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2020.1734101 ◽

2020 ◽

Vol 11 (1) ◽

pp. 510-534

Author(s):

Yuanyao Li ◽

Yifan Sheng ◽

Bo Chai ◽

Wei Zhang ◽

Taili Zhang ◽

...

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Radial Basis Function ◽

Basis Function ◽

Back Propagation ◽

Support Vector ◽

Susceptibility Assessment ◽

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Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics toTetrahymena pyriformis§

SAR and QSAR in Environmental Research ◽

10.1080/10659360600562079 ◽

2006 ◽

Vol 17 (1) ◽

pp. 75-91 ◽

Author(s):

A. Panaye ◽

B. T. Fan ◽

J. P. Doucet ◽

X. J. Yao ◽

R. S. Zhang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Comparative Study ◽

Radial Basis Function ◽

Basis Function ◽

General Regression Neural Network ◽

Support Vector ◽

Quantitative Structure ◽

General Regression

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Three Modeling Methods Application in Near-Infrared Spectra Analysis for Determination of Volatile in Lignite Coal Samples

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.1438 ◽

2014 ◽

Vol 541-542 ◽

pp. 1438-1441

Author(s):

Xiao Li Yang ◽

Fan Wang

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Near Infrared ◽

Partial Least Square ◽

Support Vector ◽

Discrete Wavelet ◽

Radial Basis ◽

We studied volatile determination in lignite coal samples using near-infrared (NIR) spectra. Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. We used discrete wavelet transform to pre-processing. To study the influence of modeling on determination of volatile for NIR analysis of lignite coal samples, we applied three techniques to build determination model, including support vector regression, partial least square regression and radial basis function neural network. Comparison of the mean absolute percentage error (MAPE) and root mean square error of prediction (RMSEP) of the models show that the models constructed with radial basis function neural network gave the best results.

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A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine

Interdisciplinary Sciences Computational Life Sciences ◽

10.1007/s12539-016-0183-6 ◽

2016 ◽

Vol 10 (2) ◽

pp. 244-250

Author(s):

Divya Anand ◽

Babita Pandey ◽

Devendra K. Pandey

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Radial Basis Function ◽

Basis Function ◽

Selection Model ◽

Support Vector ◽

Bhattacharyya Coefficient ◽

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