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Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks

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dc.contributor.author Sabir, Zulqurnain
dc.contributor.author Raja, Muhammad Asif Zahoor
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Sadat, Rahma
dc.contributor.author Ali, Mohamed R.
dc.date.accessioned 2024-03-19T12:47:23Z
dc.date.available 2024-03-19T12:47:23Z
dc.date.issued 2022
dc.identifier.citation Sabir, Zulqurnain;...et.al. (2022). "Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks", Thermal Science, Vol.26, No.4, pp.3399-3412. tr_TR
dc.identifier.issn 03549836
dc.identifier.uri http://hdl.handle.net/20.500.12416/7635
dc.description.abstract This study aims to solve the non-linear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNN) along with the optimization procedures of global search as a genetic algorithm together with the quick local search process as active-set technique (GNN-GA-AST). The GNN are executed to discretize the non-linear FO-IMM to prompt the fitness function in the procedure of mean square error. The exactness of the GNN-GA-AST is observed by comparing the obtained results with the reference results. The numerical performances of the stochastic GNN-GA-AST are provided to tackle three different variants based on the non-linear FO-IMM to authenticate the consistency, significance and efficacy of the designed stochastic GNN-GA-AST. Additionally, statistical illustrations are available to authenticate the precision, accuracy and convergence of the designed stochastic GNN-GA-AST. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.2298/TSCI210508261S tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Active-Set Technique tr_TR
dc.subject Fifth-Order Non-Linear Induction Motor Model tr_TR
dc.subject Genetic Algorithm tr_TR
dc.subject Gudermannain Neural Network tr_TR
dc.subject Statistical Measures tr_TR
dc.title Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks tr_TR
dc.type article tr_TR
dc.relation.journal Thermal Science tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 26 tr_TR
dc.identifier.issue 4 tr_TR
dc.identifier.startpage 3399 tr_TR
dc.identifier.endpage 3412 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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