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.
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