TY - JOUR
TI - Investigations of non-linear induction motor model using the Gudermannian neural networks
AU - Sabir Zulqurnain
AU - Raja Muhammad Asif Zahoor
AU - Baleanu Dumitru
AU - Sadat R
AU - Ali Mohamed R
JN - Thermal Science
PY - 2022
VL - 26
IS - 4
SP - 3399
EP - 3412
PT - Article
AB - 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.