Numerical simulation of Volterra–Fredholm integral equations using least squares support vector regression

Computational and Applied Mathematics ◽

10.1007/s40314-021-01471-0 ◽

2021 ◽

Vol 40 (7) ◽

Author(s):

K. Parand ◽

M. Hasani ◽

M. Jani ◽

H. Yari

Keyword(s):

Numerical Simulation ◽

Least Squares ◽

Support Vector Regression ◽

Integral Equations ◽

Support Vector ◽

Fredholm Integral Equations

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A new approach to the numerical solution of Fredholm integral equations using least squares-support vector regression

Mathematics and Computers in Simulation ◽

10.1016/j.matcom.2020.08.010 ◽

2021 ◽

Vol 180 ◽

pp. 114-128 ◽

Author(s):

K. Parand ◽

A.A. Aghaei ◽

M. Jani ◽

A. Ghodsi

Keyword(s):

Numerical Solution ◽

Least Squares ◽

Support Vector Regression ◽

Integral Equations ◽

Support Vector ◽

Fredholm Integral Equations ◽

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Least squares support vector regression for solving Volterra integral equations

Engineering With Computers ◽

10.1007/s00366-020-01186-6 ◽

2020 ◽

Author(s):

K. Parand ◽

M. Razzaghi ◽

R. Sahleh ◽

M. Jani

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Integral Equations ◽

Volterra Integral Equations ◽

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Improving the optimization efficiency and precision of least squares support vector regression (LSSVR) for pear property prediction

10.1117/12.865737 ◽

2010 ◽

Author(s):

Yong Hao ◽

Yande Liu ◽

Hailiang Zhang ◽

Xuemei Liu ◽

Yuanyuan Pan

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Property Prediction ◽

Optimization Efficiency

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Rapid measurement method for lithium‐ion battery state of health estimation based on least squares support vector regression

International Journal of Energy Research ◽

10.1002/er.6194 ◽

2020 ◽

Author(s):

Bin Xiao ◽

Bing Xiao ◽

Luoshi Liu

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Lithium Ion Battery ◽

Measurement Method ◽

Lithium Ion ◽

Support Vector ◽

State Of Health ◽

Rapid Measurement

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Application of QSPR for prediction of percent conversion of esterification reactions in supercritical carbon dioxide using least squares support vector regression

The Journal of Supercritical Fluids ◽

10.1016/j.supflu.2010.04.007 ◽

2010 ◽

Vol 54 (2) ◽

pp. 222-230 ◽

Author(s):

M. Esteki ◽

M. Rezayat ◽

H.S. Ghaziaskar ◽

T. Khayamian

Keyword(s):

Carbon Dioxide ◽

Supercritical Carbon Dioxide ◽

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Percent Conversion ◽

Esterification Reactions ◽

Supercritical Carbon

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Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Natural Resources Research ◽

10.1007/s11053-011-9140-6 ◽

2011 ◽

Vol 20 (2) ◽

pp. 117-129 ◽

Author(s):

Snehamoy Chatterjee ◽

Sukumar Bandopadhyay

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Space Dimension ◽

Support Vector ◽

Resource Estimation ◽

Input Space ◽

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Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.246736 ◽

2006 ◽

Author(s):

Jayadeva ◽

R. Khemchandani ◽

S. Chandra

Keyword(s):

Time Series ◽

Least Squares ◽

Support Vector Regression ◽

Time Series Forecasting ◽

Support Vector ◽

Regularized Least Squares

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Least Squares-Support Vector Regression for Determining Product Concentrations in Acid-Catalyzed Propylene Oligomerization

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.8b02539 ◽

2018 ◽

Vol 57 (39) ◽

pp. 13156-13176 ◽

Author(s):

Kaushik Sivaramakrishnan ◽

Jinjun Nie ◽

Arno de Klerk ◽

Vinay Prasad

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

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Nonlinear Calibration of Thermocouple Sensor Based on Least Squares Support Vector Regression

Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering ◽

10.2991/iccsae-15.2016.2 ◽

2016 ◽

Author(s):

Shengbo Zhang ◽

Qingling Dai

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Support Vector ◽

Nonlinear Calibration

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Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems

Korean Journal of Chemical Engineering ◽

10.1007/s11814-010-0220-9 ◽

2010 ◽

Vol 27 (4) ◽

pp. 1063-1071 ◽

Author(s):

Tae Chang Park ◽

Ui Sik Kim ◽

Lae-Hyun Kim ◽

Byung Wan Jo ◽

Yeong Koo Yeo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

District Heating ◽

Support Vector ◽

Heating Systems ◽

District Heating Systems ◽

Regression Techniques

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