Citation
Abstract
This paper attempts to create an artificial neural networks (ANNs) technique for solving well-known fractal-fractional differential equations (FFDEs). FFDEs have the advantage of being able to help explain a variety of real-world physical problems. The technique implemented in this paper converts the original differential equation into a minimization problem using a suggested truncated power series of the solution function. Next, answer to the problem is obtained via computing the parameters with highly precise neural network model. We can get a good approximate solution of FFDEs by combining the initial conditions with the ANNs performance. Examples are provided to portray the efficiency and applicability of this method. Comparison with similar existing approaches are also conducted to demonstrate the accuracy of the proposed approach.
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Official URL or Download Paper: https://link.springer.com/article/10.1007/s00366-0...
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Science Institute for Mathematical Research |
DOI Number: | https://doi.org/10.1007/s00366-022-01607-8 |
Publisher: | Springer |
Keywords: | Artificial neural network; Fractal-fractional differential equations; Back-propagation learning algorithm; New generalized Caputo fractal-fractional derivative |
Depositing User: | Ms. Nur Faseha Mohd Kadim |
Date Deposited: | 18 Mar 2024 05:08 |
Last Modified: | 18 Mar 2024 05:08 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/s00366-022-01607-8 |
URI: | http://psasir.upm.edu.my/id/eprint/100245 |
Statistic Details: | View Download Statistic |
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