A stochastic neural network procedure for the nonlinear typhoid fever disease system

Abstract

The aim of this work is to provide the numerical results of the typhoid fever disease system by applying an artificial neural network. The nonlinear typhoid fever disease system is considered into susceptible, exposed, infected, and recovered. The typhoid fever disease system is one of the nonlinear models and numerical results of the system are accomplished via stochastic computing scheme. The optimization is performed by using the Levenberg-Marquardt backpropagation (LMQBP) neural network for solving the nonlinear typhoid fever disease system. An explicit Runge-Kutta solver implemented to calculate the dataset, which is used to lessen the mean square error by data separating into testing (10%), training (70%), and validation (20%). The proposed stochastic scheme is implemented by taking sixteen neurons, log-sigmoid transfer function in the hidden layer, with the input and output layer structure for solving the typhoid fever disease system. The exactness of the scheme is validated by applying the assessment of reference and obtained outputs along with negligible values of the absolute error. Furthermore, the statistical presentations using various disciplines are implemented to indorse the approach's consistency. The proposed stochastic scheme is implemented first time to solve the nonlinear typhoid fever disease system.