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

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Date

2022

Authors

Sabir, Zulqurnain
Raja, Muhammad Asif Zahoor
Baleanu, Dumitru
Sadat, Rahma
Ali, Mohamed R.

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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.

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Keywords

Active-Set Technique, Fifth-Order Non-Linear Induction Motor Model, Genetic Algorithm, Gudermannain Neural Network, Statistical Measures

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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.

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Source

Thermal Science

Volume

26

Issue

4

Start Page

3399

End Page

3412