期刊论文详细信息
Applied Sciences
Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models
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】

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