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

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...

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Main Authors: R. Heydari Dastjerdi, G. Ahmadi
Format: Article
Language:English
Published: Ferdowsi University of Mashhad 2024-12-01
Series:Iranian Journal of Numerical Analysis and Optimization
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Online Access:https://ijnao.um.ac.ir/article_45196_621d8d6516c4ea0aff9c8a92689768ed.pdf
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author R. Heydari Dastjerdi
G. Ahmadi
author_facet R. Heydari Dastjerdi
G. Ahmadi
author_sort R. Heydari Dastjerdi
collection DOAJ
description 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.
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publisher Ferdowsi University of Mashhad
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series Iranian Journal of Numerical Analysis and Optimization
spelling doaj-art-5f8d8ee3854540f2b93ce47bfcd25ffb2025-08-20T02:21:02ZengFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772423-69692024-12-0114Issue 41016103610.22067/ijnao.2024.86494.138045196Designing the sinc neural networks to solve the fractional optimal control problemR. Heydari Dastjerdi0G. Ahmadi1Department of Mathematics, Payame Noor University, Tehran, Iran.Department of Mathematics, Payame Noor University, Tehran, Iran.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.https://ijnao.um.ac.ir/article_45196_621d8d6516c4ea0aff9c8a92689768ed.pdfsinc numerical methodneural networksinc neural networkpontryagin’s minimum principlefractional optimal control problem
spellingShingle R. Heydari Dastjerdi
G. Ahmadi
Designing the sinc neural networks to solve the fractional optimal control problem
Iranian Journal of Numerical Analysis and Optimization
sinc numerical method
neural network
sinc neural network
pontryagin’s minimum principle
fractional optimal control problem
title Designing the sinc neural networks to solve the fractional optimal control problem
title_full Designing the sinc neural networks to solve the fractional optimal control problem
title_fullStr Designing the sinc neural networks to solve the fractional optimal control problem
title_full_unstemmed Designing the sinc neural networks to solve the fractional optimal control problem
title_short Designing the sinc neural networks to solve the fractional optimal control problem
title_sort designing the sinc neural networks to solve the fractional optimal control problem
topic sinc numerical method
neural network
sinc neural network
pontryagin’s minimum principle
fractional optimal control problem
url https://ijnao.um.ac.ir/article_45196_621d8d6516c4ea0aff9c8a92689768ed.pdf
work_keys_str_mv AT rheydaridastjerdi designingthesincneuralnetworkstosolvethefractionaloptimalcontrolproblem
AT gahmadi designingthesincneuralnetworkstosolvethefractionaloptimalcontrolproblem