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Hybrid ·Peer-reviewed·ISSN (Online): 2169-012X·ISSN (Print): 0972-0502

Monthly Journal: Publishes the methodological and theoretical role of mathematics and mathematical applications underpinning scientific research.

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Open Access Research Article

Designing feed forward fully fuzzy neural network to solve fuzzy singular perturbed Volterra integro-differential equations

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pp. 139–146Vol. 28Issue 1February 2025DOI: 10.47974/JIM-1804XML
Received:
04 Jun 2024
Published Online:
13 Feb 2025
Article type:
Research Article
Language:
EN
Article no.:
JIM-1804
Pages:
139–146

Abstract

Recently, the study of singular Volterra integro-differential equations has been of increasing interest for a long time. Our paper has a design for a fast feed-forward neural network to adduce a new method for solving one-dimensions fuzzy singular perturbed integro-differential equations. Employing a multi-layer that has one hidden layer with five units and one linear output unit. And the sigmoid activation for every unit is the hyperbolic tangent function and the Levenberg-Marquardt training algorithm.We compared our exact solution in illustrative examples with the results of numerical experiments, confirming the efficiency and accuracy of our presented scheme.

Keywords

Subject Classifications

34K28

References

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