Least squares support vector regression for differential equations on unbounded domains

A. Pakniyat, K. Parand and M. Jani

Chaos, Solitons & Fractals, 2021, vol. 151, issue C

Abstract: In this paper, a numerical method based on the least-squares support vector regression, and spectral methods are developed for solving differential equations on unbounded domains. In the proposed method, Hermite functions are used as the orthogonal kernel of the support vector regression. The resulting optimization problem is then reduced to a linear system in both collocation and Galerkin approaches of the method. The systems are then analyzed, along with a discussion of the sparsity of the involving matrices. Providing some numerical examples, including fractional differential equations, the accuracy and efficiency of the method are illustrated and compared with some existing methods.

Keywords: Least squares support vector regression; Unbounded domain; Fractional differential equations; Hermite kernel; Galerkin LS-SVR; Collocation LS-SVR (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005865

DOI: 10.1016/j.chaos.2021.111232

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