Neuro-swarms intelligent computing using Gudermannian kernel for solving a class of second order Lane-Emden singular nonlinear model

AIMS Mathematics

Gilder Cieza Altamirano¹  Zulqurnain Sabir²  Muhammad Asif Zahoor Raja³  Adnène Arbi⁴  Jinde Cao⁵ 

[1] Laboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage, Tunisia;1. Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan;2. Future Research Technology Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C 3. Department of Computer and Electrical Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan;4. Department of Advanced Sciences and Technologies at National School of Advanced Sciences and Technologies of Borj Cedria;5. Department of General Studies, National Autonomous University of Chota, Perú;

关键词: lane-emden singular system;    gudermannian neural networks;    sequential quadratic scheme;    gudermannian kernel;    numerical results;    particle swarm optimization;   

DOI  :  10.3934/math.2021150

来源: DOAJ

【 摘 要 】

The present work is to design a novel Neuro swarm computing standards using artificial intelligence scheme to exploit the Gudermannian neural networks (GNN)accomplished with global and local search ability of particle swarm optimization (PSO) and sequential quadratic programming scheme (SQPS), called as GNN-PSO-SQPS to solve a class of the second order Lane-Emden singular nonlinear model (SO-LES-NM). The suggested intelligent computing solver GNN-PSO-SQPS using the Gudermannian kernel are unified with the configuration of the hidden layers of GNN of differential operators for solving the SO-LES-NM. An error based fitness function (FF) applying the differential form of the differential model and corresponding boundary conditions. The FF is optimized together with the combined heuristics of PSO-SQPS. Three problems of the SO-LES-NM are solved to validate the correctness, effectiveness and competence of the designed GNN-PSO-SQPS. The performance of the GNN-PSO-SQPS through statistical operators is tested to check the constancy, convergence and precision.

【 授权许可】

Unknown