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A new method based on least-squares support vector regression for solving optimal control problems
Kybernetika, Tome 60 (2024) no. 4, pp. 513-534
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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.
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.
DOI : 10.14736/kyb-2024-4-0513
Classification : 49Mxx, 68T20
Keywords: Least squares support vector machines; Optimal control problems; Legendre orthogonal polynomials; Regression; Artificial intelligence 
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Bolhassani, Mitra; Dana Mazraeh, Hassan; Parand, Kourosh. A new method based on least-squares support vector regression for solving optimal control problems. Kybernetika, Tome 60 (2024) no. 4, pp. 513-534. doi: 10.14736/kyb-2024-4-0513

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