Raja, Muhammad Asif ZahoorBaleanu, DumitruSadat, RahmaAli, Mohamed R.Sabir, Zulqurnain2024-03-192025-09-182024-03-192025-09-182022Sabir, Zulqurnain;...et.al. (2022). "Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks", Thermal Science, Vol.26, No.4, pp.3399-3412.0354-98362334-7163https://doi.org/10.2298/TSCI210508261Shttps://hdl.handle.net/123456789/12051Sabir, Zulqurnain/0000-0001-7466-6233; Raja, Muhammad Asif Zahoor/0000-0001-9953-822X; Ali, Mohamed/0000-0002-0795-0709This 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.eninfo:eu-repo/semantics/openAccessFifth-Order Non-Linear Induction Motor ModelActive-Set TechniqueGudermannain Neural NetworkGenetic AlgorithmStatistical MeasuresArticle10.2298/TSCI210508261S2-s2.0-85135516747