Numerical solution of a class of singular boundary value problems arising in physiology based on neural networks

Neha Yadav, Joong Hoon Kim, Anupam Yadav

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a soft computing approach based on neural networks is presented for the numerical solution of a class of singular boundary value problems (SBVP) arising in physiology. The mathematical model of artificial neural network (ANN) is developed in a way to satisfy the boundary conditions exactly using log-sigmoid activation function in hidden layers. Training of the neural network parameters was performed by gradient descent backpropagation algorithm with sufficient number of independent runs. Two test problems from physical applications have been considered to check the accuracy and efficiency of the method. Proposed results for the solution of SBVP have been compared with the exact analytical solution as well as the solution obtained by the existing numerical methods and shows good agreement with others.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages673-681
Number of pages9
Volume437
ISBN (Print)9789811004506
DOIs
Publication statusPublished - 2016
Event5th International Conference on Soft Computing for Problem Solving, SocProS 2015 - Roorkee, India
Duration: 2015 Dec 182015 Dec 20

Publication series

NameAdvances in Intelligent Systems and Computing
Volume437
ISSN (Print)21945357

Other

Other5th International Conference on Soft Computing for Problem Solving, SocProS 2015
CountryIndia
CityRoorkee
Period15/12/1815/12/20

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Keywords

  • Gradient descent algorithm
  • Neural networks
  • Singular boundary value problem

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Yadav, N., Kim, J. H., & Yadav, A. (2016). Numerical solution of a class of singular boundary value problems arising in physiology based on neural networks. In Advances in Intelligent Systems and Computing (Vol. 437, pp. 673-681). (Advances in Intelligent Systems and Computing; Vol. 437). Springer Verlag. https://doi.org/10.1007/978-981-10-0451-3_60