Abstract
This study presents an innovative application of Feedforward Neural Networks ‘FNNs’ to solve Variable-Order Fractional Partial Differential Equations ‘VO-FPDEs’ with time delays. Utilizing the Caputo definition, the variable-order fractional derivatives are approximated in terms of integer-order derivatives. The problem is reformulated as a system of partial differential equations with delay terms, which is then addressed using ‘FNNs’ to achieve explicit approximate solutions. Comprehensive error and convergence analyses validate the method’s precision and reliability. The effectiveness of the proposed approach is highlighted through numerical examples, with graphical and tabular representations showcasing minimal absolute errors and robust convergence. These results demonstrate the proposed method’s efficiency and simplicity, establishing it as a powerful tool for addressing complex fractional delay problems.
Reason for Retraction
This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction Guidelines.
Please see the Retraction Notice available at: https://ijcsm.researchcommons.org/ijcsm/vol7/iss1/31/
Recommended Citation
Alruhaili, Hala S.; Hussain, Adel S.; Ajlouni, Abdullah M. S.; Türk, Funda; Az-Zo’bi, Emad A.; and Tashtoush, Mohammad A.
(2025)
"Retracted: Solving Time-Fractional Nonlinear Variable-Order Delay PDEs Using Feedforward Neural Networks,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
3, Article 12.
DOI: https://doi.org/10.52866/2788-7421.1284
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/12

