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Neural Computing and Applications

Erschienen in:

16.08.2021 | Original Article

A neural network training algorithm for singular perturbation boundary value problems

verfasst von: T. E. Simos, Ioannis Th. Famelis

Erschienen in: Neural Computing and Applications | Ausgabe 1/2022

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Abstract

A training algorithm for the Neural Network solution of Singular Perturbation Boundary Value Problems is presented. The solution is based on a single hidden layer feed forward Neural Network with a small number of neurons. The training algorithm adapts the training points grid so to be more tense in areas of the integration interval that solution has a layer or a peek. The algorithm automatically detects the areas of interest in the integration interval. The resulted Neural Network solutions are very accurate in a uniform way. The numerical tests in various test problems justify our arguments as the produced solutions prove to give smaller errors compare to their competitors.

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Titel: A neural network training algorithm for singular perturbation boundary value problems
verfasst von: T. E. Simos
Ioannis Th. Famelis
Publikationsdatum: 16.08.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-06364-1

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