THERMAL SCIENCE

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Investigations of nonlinear induction motor model using the Gudermannian neural networks

ABSTRACT
This study aims to solve the nonlinear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNNs) 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). GNNs are executed to discretize the nonlinear 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 nonlinear 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.
KEYWORDS
PAPER SUBMITTED: 2021-05-08
PAPER REVISED: 2021-08-16
PAPER ACCEPTED: 2021-08-21
PUBLISHED ONLINE: 2021-09-04
DOI REFERENCE: https://doi.org/10.2298/TSCI210508261S
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