Investigations of non-linear induction motor model using the Gudermannian neural networks

This study aims to solve the non-linear fifth-order induction motor model (FO-IMM) using Gudermannian neural networks (GNN) along with optimization procedures of global search as a genetic algorithm together quick local process active-set technique (GNN-GA-AST). The GNN are executed discretize FO-IMM prompt fitness function in procedure mean square error. exactness GNN-GA-AST is observed by comparing obtained results reference results. numerical performances stochastic provided tackle three different variants based on authenticate consistency, significance and efficacy designed GNN-GA-AST. Additionally, statistical illustrations available precision, accuracy convergence