Strain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference System

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dc.authorid0000-0002-9735-5697en_US
dc.authorid0000-0001-8456-1478en_US
dc.authorid0000-0003-2989-3781en_US
dc.contributor.authorEyecioğlu, Önder
dc.contributor.authorKayışlı, Korhan
dc.contributor.authorBeken, Murat
dc.date.accessioned2024-07-02T10:10:53Z
dc.date.available2024-07-02T10:10:53Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSingle Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.en_US
dc.identifier.citationEyecioglu, O., Kayisli, K., & Beken, M. (2024). Strain Energy Prediction of Single Wall Carbon Nanotubes Using General Regression Neural Network and Adaptive Neuro–Fuzzy Inference System. Electric Power Components and Systems, 52(8), 1437-1447.en_US
dc.identifier.doi10.1080/15325008.2023.2286346
dc.identifier.endpage1447en_US
dc.identifier.issn1532-5008
dc.identifier.issn1532-5016
dc.identifier.scopus2-s2.0-85179723318en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1437en_US
dc.identifier.urihttp://dx.doi.org/10.1080/15325008.2023.2286346
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12233
dc.identifier.wosWOS:001123890300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEyecioğlu, Önder
dc.institutionauthorBeken, Murat
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofElectric Power Components and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCarbon Nanotubesen_US
dc.subjectComputer Simulationen_US
dc.subjectElectronic-Structureen_US
dc.subjectSimulationen_US
dc.subjectDiffusionen_US
dc.titleStrain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference Systemen_US
dc.typeArticleen_US

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