Neuro-computing solution for Lorenz differential equations through artificial neural networks integrated with PSO-NNA hybrid meta-heuristic algorithms: a comparative study

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dc.authoridArshad, Muhammad Sarmad/0000-0001-8223-8183
dc.contributor.authorAslam, Muhammad Naeem
dc.contributor.authorAslam, Muhammad Waheed
dc.contributor.authorArshad, Muhammad Sarmad
dc.contributor.authorAfzal, Zeeshan
dc.contributor.authorHassani, Murad Khan
dc.contributor.authorZidan, Ahmed M.
dc.contributor.authorAkgul, Ali
dc.date.accessioned2024-12-24T19:27:57Z
dc.date.available2024-12-24T19:27:57Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this article, examine the performance of a physics informed neural networks (PINN) intelligent approach for predicting the solution of non-linear Lorenz differential equations. The main focus resides in the realm of leveraging unsupervised machine learning for the prediction of the Lorenz differential equation associated particle swarm optimization (PSO) hybridization with the neural networks algorithm (NNA) as ANN-PSO-NNA. In particular embark on a comprehensive comparative analysis employing the Lorenz differential equation for proposed approach as test case. The nonlinear Lorenz differential equations stand as a quintessential chaotic system, widely utilized in scientific investigations and behavior of dynamics system. The validation of physics informed neural network (PINN) methodology expands to via multiple independent runs, allowing evaluating the performance of the proposed ANN-PSO-NNA algorithms. Additionally, explore into a comprehensive statistical analysis inclusive metrics including minimum (min), maximum (max), average, standard deviation (S.D) values, and mean squared error (MSE). This evaluation provides found observation into the adeptness of proposed AN-PSO-NNA hybridization approach across multiple runs, ultimately improving the understanding of its utility and efficiency.
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [RGP.2/13/44]
dc.description.sponsorshipThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP.2/13/44.
dc.identifier.doi10.1038/s41598-024-56995-2
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid38553496
dc.identifier.scopus2-s2.0-85188909302
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-024-56995-2
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6849
dc.identifier.volume14
dc.identifier.wosWOS:001195862400049
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectArtificial neural networks (ANN)
dc.subjectChaotic system
dc.subjectParticle swarm optimization (PSO)
dc.subjectNeural network algorithm (NNA)
dc.subjectHybrid approach
dc.titleNeuro-computing solution for Lorenz differential equations through artificial neural networks integrated with PSO-NNA hybrid meta-heuristic algorithms: a comparative study
dc.typeArticle

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