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Multilayered neural network for power seriesābased approximation of fractional delay differential equations.
- Source :
-
Mathematical Methods in the Applied Sciences . 7/30/2024, Vol. 47 Issue 11, p8771-8785. 15p. - Publication Year :
- 2024
-
Abstract
- This paper trains a multilayered neural network (MLNN) for solving fractional delay differential equations (FDDEs), including nonlinear and singular types. The proposed methodology involves replacing the unknown functions in the equations with a truncated power series expansion. Subsequently, a collection of algebraic equations is solved utilizing an iterative minimization technique that leverages the capabilities of the MLNN architecture. The outcomes demonstrate that the MLNN architecture provides the required accuracy and strong stability compared to several numerical methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01704214
- Volume :
- 47
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Mathematical Methods in the Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 177773288
- Full Text :
- https://doi.org/10.1002/mma.10043