Evolutionary integrated heuristic with gudermannian neural networks for second kind of lane– emden nonlinear singular models
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 activ...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
MDPI
2021
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/30009/1/Evolutionary%20integrated%20heuristic%20with%20gudermannian%20neural%20networks%20for%20second%20kind%20of%20lane%E2%80%93emden%20nonlinear%20singular%20models-Abstract.pdf https://eprints.ums.edu.my/id/eprint/30009/2/Evolutionary%20integrated%20heuristic%20with%20gudermannian%20neural%20networks%20for%20second%20kind%20of%20lane%E2%80%93emden%20nonlinear%20singular%20models.pdf |
| Summary: | 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 |
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