A new method based on least-squares support vector regression for solving optimal control problems

Mitra Bolhassani; Hassan Dana Mazraeh; Kourosh Parand

Kybernetika (2024)

  • Issue: 4, page 513-534
  • ISSN: 0023-5954

Abstract

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In this paper, a new application of the Least Squares Support Vector Regression (LS-SVR) with Legendre basis functions as mapping functions to a higher dimensional future space is considered for solving optimal control problems. At the final stage of LS-SVR, an optimization problem is formulated and solved using Maple optimization packages. The accuracy of the method are illustrated through numerical examples, including nonlinear optimal control problems. The results demonstrate that the proposed method is capable of solving optimal control problems with high accuracy.

How to cite

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Bolhassani, Mitra, Dana Mazraeh, Hassan, and Parand, Kourosh. "A new method based on least-squares support vector regression for solving optimal control problems." Kybernetika (2024): 513-534. <http://eudml.org/doc/299390>.

@article{Bolhassani2024,
abstract = {In this paper, a new application of the Least Squares Support Vector Regression (LS-SVR) with Legendre basis functions as mapping functions to a higher dimensional future space is considered for solving optimal control problems. At the final stage of LS-SVR, an optimization problem is formulated and solved using Maple optimization packages. The accuracy of the method are illustrated through numerical examples, including nonlinear optimal control problems. The results demonstrate that the proposed method is capable of solving optimal control problems with high accuracy.},
author = {Bolhassani, Mitra, Dana Mazraeh, Hassan, Parand, Kourosh},
journal = {Kybernetika},
keywords = {Least squares support vector machines; Optimal control problems; Legendre orthogonal polynomials; Regression; Artificial intelligence},
language = {eng},
number = {4},
pages = {513-534},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A new method based on least-squares support vector regression for solving optimal control problems},
url = {http://eudml.org/doc/299390},
year = {2024},
}

TY - JOUR
AU - Bolhassani, Mitra
AU - Dana Mazraeh, Hassan
AU - Parand, Kourosh
TI - A new method based on least-squares support vector regression for solving optimal control problems
JO - Kybernetika
PY - 2024
PB - Institute of Information Theory and Automation AS CR
IS - 4
SP - 513
EP - 534
AB - In this paper, a new application of the Least Squares Support Vector Regression (LS-SVR) with Legendre basis functions as mapping functions to a higher dimensional future space is considered for solving optimal control problems. At the final stage of LS-SVR, an optimization problem is formulated and solved using Maple optimization packages. The accuracy of the method are illustrated through numerical examples, including nonlinear optimal control problems. The results demonstrate that the proposed method is capable of solving optimal control problems with high accuracy.
LA - eng
KW - Least squares support vector machines; Optimal control problems; Legendre orthogonal polynomials; Regression; Artificial intelligence
UR - http://eudml.org/doc/299390
ER -

References

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