Artificial neural network scheme to solve the nonlinear influenza disease model

The aim of this study is to present the numerical simulations of the influenza disease nonlinear system (IDNS) using the stochastic artificial neural networks (ANNs) procedures supported with Levenberg-Marquardt back-propagation (LMB), i.e., ANNs-LMB. The IDNS is constructed with four classes, susceptible S(t), infected I(t), recovered R(t) and cross-immune people C(t), based stiff nonlinear ordinary differential system. The numerical computations have been performed through the stochastic ANNs-LMB for solving six different variations of the IDNS. The obtained numerical solutions through the stochastic ANNs-LMB for solving the IDNS have been presented using the training, verification and testing measures to reduce mean square error (MSE) from data-based reference solutions. To observed the correctness, efficiency, competence and proficiency of the designed computing paradigm ANNs-LMB, an exhaustive analysis is presented using the correlation studies, error histograms (EHs), mean squared error (MSE), regression and state transitions (STs) information. The worth and significance of ANNs-LMB is substantiated through comparisons of the outcomes admitted the good agreement from data derived results with 5-7 decimal places of accuracy for each scenario of IDNS.

Automated summary

This study presents numerical simulations of the influenza disease nonlinear system using stochastic artificial neural networks supported by Levenberg-Marquardt back-propagation. The results show that the model is able to accurately and efficiently solve different variations of the IDNS, achieving good agreement with data-derived results to 5-7 decimal places of accuracy.