Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Applied Sciences

DandaB. Rawat¹  Manoj Gupta²  Zulqurnain Sabir³  Aldawoud Kamal⁴  Adnan Shahid Khan⁵  Ag.Asri Ag. Ibrahim⁶  Kashif Nisar⁶  MuhammadAsif Zahoor Raja⁷  JoelJ. P. C. Rodrigues⁸ 

[1] Data Science and Cybersecurity Center, Dept of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA;Department of Electronics and Communication Engineering, JECRC University Jaipur, Rajasthan 303905, India;Department of Mathematics and Statistics, Hazara University, Mansehra 21120, Pakistan;Department of Mathematics and Statistics, Mutah University Jordan, Mu’tah 61710, Jordan;Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah 88400, Malaysia;Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan;Post-Graduation Program in Electrical Engineering, Federal University of Piauí (UFPI), Teresina-PI 64049-550, Brazil;

关键词: Gudermannian kernel;    Lane–Emden model;    Gudermannian neural networks;    active-set method;    numerical solutions;    genetic algorithms;   

DOI  :  10.3390/app11114725

来源: DOAJ

【 摘 要 】

In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

【 授权许可】

Unknown