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The Possibility of Neural Network Approach to Solve Singular Perturbed Problems

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(1), pp.69-76
  • DOI : 10.9708/jksci.2021.26.01.069
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 29, 2020
  • Accepted : January 20, 2021
  • Published : January 29, 2021

Kim, JeeHyun 1 Young Im Cho 2

1서일대학교
2가천대학교

Accredited

ABSTRACT

Recentlly neural network approach for solving a singular perturbed integro-differential boundary value problem have been researched. Especially the model of the feed-forward neural network to be trained by the back propagation algorithm with various learning algorithms were theoretically substantiated, and neural network models such as deep learning, transfer learning, federated learning are very rapidly evolving. The purpose of this paper is to study the approaching method for developing a neural network model with high accuracy and speed for solving singular perturbed problem along with asymptotic methods. In this paper, we propose a method that the simulation for the difference between result value of singular perturbed problem and unperturbed problem by using neural network approach equation. Also, we showed the efficiency of the neural network approach. As a result, the contribution of this paper is to show the possibility of simple neural network approach for singular perturbed problem solution efficiently.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.