Design of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numerically

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dc.authoridRaja, Muhammad Asif Zahoor/0000-0001-9953-822X
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridBaleanu, Dumitru/0000-0002-0286-7244
dc.authoridsabir, zulqurnain/0000-0001-7466-6233
dc.authorwosidAhmad, Mirza Iftikhar/KHD-2053-2024
dc.authorwosidAwais, Muhammad/HCI-3725-2022
dc.authorwosidShoaib, Muhammad/ABB-8901-2021
dc.authorwosidRaja, Muhammad Asif Zahoor/D-7325-2013
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidBaleanu, Dumitru/B-9936-2012
dc.authorwosidsabir, zulqurnain/AAS-8882-2021
dc.contributor.authorSabir, Zulqurnain
dc.contributor.authorRaja, Muhammad Asif Zahoor
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorCengiz, Korhan
dc.contributor.authorShoaib, Muhammad
dc.date.accessioned2024-06-12T11:02:51Z
dc.date.available2024-06-12T11:02:51Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe current investigation is related to the design of novel integrated neuroswarming heuristic paradigm using Gudermannian artificial neural networks (GANNs) optimized with particle swarm optimization (PSO) aid with active-set (AS) algorithm, i.e., GANN-PSOAS, for solving the nonlinear third-order Emden-Fowler model (NTO-EFM) involving single as well as multiple singularities. The Gudermannian activation function is exploited to construct the GANNs-based differential mapping for NTO-EFMs, and these networks are arbitrary integrated to formulate the fitness function of the system. An objective function is optimized using hybrid heuristics of PSO with AS, i.e., PSOAS, for finding the weights of GANN. The correctness, effectiveness and robustness of the designed GANN-PSOAS are verified through comparison with the exact solutions on three problems of NTO-EFMs. The assessments on statistical observations demonstrate the performance on different measures for the accuracy, consistency and stability of the proposed GANN-PSOAS solver.en_US
dc.identifier.doi10.1007/s11071-021-06901-6
dc.identifier.endpage3214en_US
dc.identifier.issn0924-090X
dc.identifier.issn1573-269X
dc.identifier.issue4en_US
dc.identifier.pmid34785862en_US
dc.identifier.scopus2-s2.0-85118858995en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3199en_US
dc.identifier.urihttps://doi.org/10.1007/s11071-021-06901-6
dc.identifier.urihttps://hdl.handle.net/20.500.14551/21444
dc.identifier.volume106en_US
dc.identifier.wosWOS:000717399900002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNonlinear Dynamicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGudermannian Functionen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectEmden-Fowleren_US
dc.subjectActive-Set Schemeen_US
dc.subjectStatistical Analysisen_US
dc.subjectNeural-Networken_US
dc.subjectAlgorithmen_US
dc.subjectDynamicsen_US
dc.subjectSearchen_US
dc.titleDesign of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numericallyen_US
dc.typeArticleen_US

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