Design of intelligent computing solver with Morlet wavelet neural networks for nonlinear predator–prey model.

The design of integrated intelligent computing solver with Morlet wavelet neural networks (MW-NNs) is presented for solving the mathematical predator–prey model by exploiting the strength of MW-NNs modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NNs-GA-SQP. The proposed MW-NNs-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. The validation, correctness and reliability of the presented MW-NNs-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. Moreover, statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NNs-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. • The design of integrated intelligent computing solver with Morlet wavelet neural network (MW-NN) is presented for solving the mathematical predator–prey model. • We use the strength of MN-NN modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NN-GA-SQP. • The proposed MW-NN-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. • The validation, correctness and reliability of the presented MWNN-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. • Statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NN-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. [ABSTRACT FROM AUTHOR]