Investigations Of Non-Linear Induction Motor Model Using The Gudermannıan Neural Networks
Loading...
Date
2022
Authors
Sabir, Zulqurnain
Raja, Muhammad Asif Zahoor
Baleanu, Dumitru
Sadat, Rahma
Ali, Mohamed R.
Journal Title
Journal ISSN
Volume Title
Publisher
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
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.
Description
Keywords
Active-Set Technique, Fifth-Order Non-Linear Induction Motor Model, Genetic Algorithm, Gudermannain Neural Network, Statistical Measures
Turkish CoHE Thesis Center URL
Fields of Science
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.
WoS Q
Scopus Q
Source
Thermal Science
Volume
26
Issue
4
Start Page
3399
End Page
3412