Open AccessInvestigations of non-linear induction motor model using the Gudermannian neural networks
Author(s) -
Zulqurnain Sabir,
Muhammad Asif Raja Zahor,
Dumitru Băleanu,
R. Sadat,
Mohamed R. Ali
Publication year - 2022
Publication title -
thermal science/thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci210508261s
Subject(s) - consistency (knowledge bases) , computer science , artificial neural network , genetic algorithm , discretization , induction motor , algorithm , fitness function , set (abstract data type) , convergence (economics) , mathematics , artificial intelligence , machine learning , physics , mathematical analysis , quantum mechanics , voltage , economics , programming language , economic growth
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.