## THERMAL SCIENCE

International Scientific Journal

### 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**

PAPER SUBMITTED: 2021-05-08

PAPER REVISED: 2021-08-16

PAPER ACCEPTED: 2021-08-21

PUBLISHED ONLINE: 2021-09-04

**THERMAL SCIENCE** YEAR

**2022**, VOLUME

**26**, ISSUE

**Issue 4**, PAGES [3399 - 3412]

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