Abstract
The purpose of the current investigation is to design a novel radial basis neural network for solving the dynamical hepatitis C virus model in patients with a high baseline viral load, which represents the nonlinear dynamical structure. The infection and treatment in the hepatitis C virus comprise uninfected hepatocytes, creatively infected hepatocytes, and viruses. The aim of this study is to solve the dynamical hepatitis C virus model in patients with a high baseline viral load with the optimization of the Bayesian regularization scheme. A database reference solution is achieved by the explicit Runge–Kutta in interval 0 and 1 with the step size of 0.01 by data division into training as 72%, while 14%, 14% for endorsement, and testing. Twenty numbers of neurons, a feed forward neural network, activation radial basis function, and the optimization Bayesian regularization approach have been used to solve the hepatitis C virus model. The precision of the scheme is perceived by the outcomes overlapping and the reducible absolute error values, which are found as 10 –06 to 10 –08 . A statistical evaluation utilizing various operators and proportional approaches is carried out in order to assess the solver’s efficiency.