A Design of Predictive Intelligent Networks for the Analysis of Fractional Model of TB-Virus

Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different order of derivatives, ranging from 0.8, 0.85, 0.9, 0.95, and 1, as well as five potential rates for different parameters. Training data made up 90% of the data, testing data made up 5%, and validation data made up 5% of the data used to illustrate the FMTBV’s approximations. To verify that the BRA-NNs were correct, the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB. Comprehensive Simulink results in terms of mean square error, error histogram, and regression analysis investigations further highlight the competence, dependability, and accuracy of the suggested BRA-NNs.