Designing the sinc neural networks to solve the fractional optimal control problem

Autor:	R. Heydari Dastjerdi, G. Ahmadi
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	sinc numerical method neural network sinc neural network pontryagin’s minimum principle fractional optimal control problem Applied mathematics. Quantitative methods T57-57.97
Zdroj:	Iranian Journal of Numerical Analysis and Optimization, Vol 14, Iss Issue 4, Pp 1016-1036 (2024)
Druh dokumentu:	article
ISSN:	2423-6977 2423-6969
DOI:	10.22067/ijnao.2024.86494.1380
Popis:	Sinc numerical methods are essential approaches for solving nonlinear problems. In this work, based on this method, the sinc neural networks (SNNs) are designed and applied to solve the fractional optimal control problem (FOCP) in the sense of the Riemann–Liouville (RL) derivative. To solve the FOCP, we first approximate the RL derivative using Grunwald–Letnikov operators. Then, according to Pontryagin’s minimum principle for FOCP and using an error function, we construct an unconstrained minimization problem. We approximate the solution of the ordinary differential equation obtained from the Hamiltonian condition using the SNN. Simulation results show the efficiencies of the proposed approach.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5f8d8ee3854540f2b93ce47bfcd25ffb Zobrazit plný text záznamu View record in DOAJ