Comparative Analysis of Picard and Adomian Decomposition Methods for Solving Fractional Differential Equations in a Neural Network Model | ||||
| Delta University Scientific Journal | ||||
| Volume 7, Issue 3, November 2024, Page 281-290 PDF (789.04 K) | ||||
| Document Type: Original research papers | ||||
| DOI: 10.21608/dusj.2024.433471 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Monica Botros  1; Eman Ziada2
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| 1Department of Basic science, Faculty of Engineering, Delta University for science and technology, Mansoura. Egypt, | ||||
| 2Basic Science Department, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt | ||||
| Abstract | ||||
| Various fields of science and engineering use neural network technology to solve their problems. In this paper, the Adomian decomposition method (ADM) is applied to solve fractional differential equations (FDEs) of a deferred correction network (DC Net) model using Caputo-Fabrizo (CF). To improve the accuracy of the calculated solution, we compare it with the Picard method (PM). It was found that the two schemes are very close to each other based on the analytical results. Comparing these two approaches, numerical tests confirm the accuracy of the proposed (DC Net) model. | ||||
| Keywords | ||||
| DC Net; Fractional model; Adomian decomposition method and Picard method; Convergence analysis; Existence and uniqueness; Caputo-Fabrizio derivative | ||||
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