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Neural Computing and Applications

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16-08-2021 | Original Article

A neural network training algorithm for singular perturbation boundary value problems

Authors: T. E. Simos, Ioannis Th. Famelis

Published in: Neural Computing and Applications | Issue 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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Title: A neural network training algorithm for singular perturbation boundary value problems
Authors: T. E. Simos
Ioannis Th. Famelis
Publication date: 16-08-2021
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-06364-1

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