Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

FMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systems

dc.contributor.authorSabir, Zulqurnain
dc.contributor.authorRaja, Muhammad Asif Zahoor
dc.contributor.authorUmar, Muhammad
dc.contributor.authorShoaib, Muhammad
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2022-04-29T12:58:21Z
dc.date.available2022-04-29T12:58:21Z
dc.date.issued2022
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractThe fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane–Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and polytropic fluid. The present study aimed to present a neuro-swarm-based intelligent computing solver for the solution of nonlinear fractional Lane–Emden system (NFLES) using by exploitation of fractional Meyer wavelet artificial neural networks (FMW-ANNs) and global optimization mechanism of particle swarm optimization (PSO) combined with rapid local search of sequential quadratic programming (SQP), i.e., FMW-ANN-PSO-SQP. The motivation for the design of FMW-ANN-PSO-SQP intelligent computing comes with an objective of presenting an accurate, reliable, and viable framworks to deal with stiff nonlinear singular models represented with NFLES involving both fractional and integer derivative terms. The designed algorithm is tested for six different variants of NFLESs. The obtained numerical outcomes obtained by the proposed FMW-ANN-PSO-SQP are compared with the exact results to authenticate the correctness, efficacy, and viability, and these aspects are further endorsed statistical observations. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.description.publishedMonth3
dc.identifier.citationSabir, Zulqurnain...et al. (2022). "FMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systems", Neural Computing and Applications, Vol. 34, No. 6, pp. 4193-4206.en_US
dc.identifier.doi10.1007/s00521-021-06452-2
dc.identifier.endpage4206en_US
dc.identifier.issn0941-0643
dc.identifier.issue6en_US
dc.identifier.startpage4193en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/5469
dc.identifier.volume34en_US
dc.language.isoenen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectFractional Lane–Emden Modelen_US
dc.subjectIntelligent Computingen_US
dc.subjectMeyer Waveletsen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectSequential Quadratic Programmingen_US
dc.titleFMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systemstr_TR
dc.titleFMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: