THERMAL SCIENCE

International Scientific Journal

Zulqurnain Sabir

,

Muhammad Asif Zahoor Raja

,

Dumitru Baleanu

,

R. Sadat

,

Mohamed R. Ali

INVESTIGATIONS OF NON-LINEAR INDUCTION MOTOR MODEL USING THE GUDERMANNIAN NEURAL NETWORKS

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.
KEYWORDS
Gudermannain neural network, Fifth-order nonlinear induction motor model, genetic algorithm, Statistical measures, Active-set technique
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
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 4, PAGES [3399 - 3412]
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Investigations of non-linear induction motor model using the Gudermannian neural networks

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