Neuro-evolution computing for nonlinear multi-singular system of third order Emden-Fowler equation

Sabir Z., Raja M. A. Z., Khalique C. M., Unlu C.

MATHEMATICS AND COMPUTERS IN SIMULATION, vol.185, pp.799-812, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 185
  • Publication Date: 2021
  • Doi Number: 10.1016/j.matcom.2021.02.004
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, Public Affairs Index, zbMATH
  • Page Numbers: pp.799-812
  • Istanbul University Affiliated: Yes


In this paper, a neuro-evolution based numerical computing approach is presented for the solution of nonlinear third order multi-singular Emden-Fowler system of differential equations (MS-EF-SDEs) by manipulating the proficiency of continuous mapping through exploitation of feed-forward artificial neural networks (ANN). The weights or decision variables of these networks are optimized with genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., ANN-GA-SQP. An error based figure of merit is introduced using the differential model of MS EF-SDE along with corresponding boundary conditions. The objective/cost function is optimized by integrating capability of global and local search with GA and SQP, respectively. The competency of the designed ANN-GA-SQP approach in terms of significance, efficiency and consistency is perceived by solving MS-EF-SDEs. Moreover, statistical based investigations are implemented to validate the correctness of ANN-GA-SQP. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.