Design of Morlet wavelet neural network to solve the non-linear influenza disease system

In this study, the solution of the non-linear influenza disease system (NIDS) is presented using the Morlet wavelet neural networks (MWNNs) together with the optimisation procedures of the hybrid process of global/local search approaches. The genetic algorithm (GA) and sequential quadratic programming (SQP), that is, GA-SQP, are executed as the global and local search techniques. The mathematical form of the NIDS depends upon four groups: susceptible S(y), infected I(y), recovered R(y) and cross-immune individuals C(y). To solve the NIDS, an error function is designed using NIDS and its leading initial conditions (ICs). This error function is optimised with a combination of MWNNs and GA-SQP to solve for all the groups of NIDS. The comparison of the obtained solutions and Runge-Kutta results is presented to authenticate the correctness of the designed MWNNs along with the GA-SQP for solving NIDS. Moreover, the statistical operators using different measures are presented to check the reliability and constancy of the MWNNs along with the GA-SQP to solve the NIDS.

Automated summary

This study presents the solution of the non-linear influenza disease system using Morlet wavelet neural networks and a combination of global/local search techniques. The designed approach is validated through comparisons with Runge-Kutta results, demonstrating its correctness and reliability in solving the NIDS. Statistical operators are used to assess the constancy and reliability of the approach, further confirming its effectiveness.