Heuristic computing with active set method for the nonlinear Rabinovich-Fabrikant model.

Zulqurnain Sabir, Dumitru Baleanu, Sharifah E Alhazmi, Salem Ben Said

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Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence.Scientific reports · 2024
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4 authors.

Zulqurnain SabirDepartment of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.

Dumitru BaleanuDepartment of Mathematics, Cankaya University, Ankara, Turkey.

Sharifah E AlhazmiMathematics Department, Al-Qunfudah University College, Umm Al-Qura University, Mecca, USA.

Salem Ben SaidDepartment of Mathematical Sciences, UAE University, P. O. Box 15551, Al Ain, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current study shows a reliable stochastic computing heuristic approach for solving the nonlinear Rabinovich-Fabrikant model. This nonlinear model contains three ordinary differential equations. The process of stochastic computing artificial neural networks (ANNs) has been applied along with the competences of global heuristic genetic algorithm (GA) and local search active set (AS) methodologies, i.e., ANNs-GAAS. The construction of merit function is performed through the differential Rabinovich-Fabrikant model. The results obtained through this scheme are simple, reliable, and accurate, which have been calculated to optimize the merit function by using the GAAS method. The comparison of the obtained results through this scheme and the conventional reference solutions strengthens the correctness of the proposed method. Ten numbers of neurons along with the log-sigmoid transfer function in the neural network structure have been used to solve the model. The values of the absolute error are performed around 10

Indexed as

Active set methodArtificial neural networksGenetic algorithmNumerical solutionsRabinovich-Fabrikant

Identifiers

PMID38034676

DOI10.1016/j.heliyon.2023.e22030

PMCPMC10682130

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.