Chelyshkov least squares support vector regression for nonlinear stochastic differential equations by variable fractional Brownian motion

P. Rahimkhani and Y. Ordokhani

Chaos, Solitons & Fractals, 2022, vol. 163, issue C

Abstract: The main aim of this study is to introduce an efficient method based on the Chelyshkov polynomials and least squares support vector regression (LS-SVR) for solving a class of nonlinear stochastic differential equations (SDEs) by variable fractional Brownian motion (VFBm). The derivative operational matrix and variable-order fractional integral operator of Chelyshkov polynomials (ChPs) are obtained. These operators, the standard Brownian motion with help of the Gauss–Legendre quadrature are applied for generating VFBm. We apply the Chelyshkov polynomials kernel and the collocation LS-SVR method for training the network. Then, the formulation of the scheme gives rise to an optimization problem. Finally, the classical optimization and Newton’s iterative scheme are used to train this problem. Moreover, we discuss convergence and error analysis of mentioned scheme. In the end, to reveal the superiority and efficiency of current paper, some test problems are applied.

Keywords: Chelyshkov polynomials; Stochastic differential equations; Fractional Brownian motion; Least squares support vector regression; Convergence analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:163:y:2022:i:c:s0960077922007615

DOI: 10.1016/j.chaos.2022.112570

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