A stochastic computing procedure to solve the dynamics of prevention in HIV system

The motive of this work is to find the numerical simulations of a dynamical HIV model along with the effects of prevention, i.e., HIPV nonlinear mathematical system. An advance computational framework using the procedures of Meyer neural networks (MNNs) together with the compotnecies of local/global search approaches is presented to solve the HIPV nonlinear mathematical system. The global and local operators will be used as genetic algorithm (GA) and interor-point algorithm (IPA), i.e., GAIPA. The dynamicis of HIPV mathematical system is classified into four categories, ‘T-cells attentiveness’, ‘Infected from disease, ‘Prevention actions’ and ‘Virus free particles. An error function is constructed using the differential system and its boundary conditions. The optimization of this function is presented through the hybridization computing paradigms of MWNNs-GAIPA. The correctness of the designed MWNNs-GAIPA is obtained by using the comparion of the obtained and reference solutions. The performance of this scheme is also acheived through the overlapping of the results with the accuracy of order 5 t 7 in the plots of absolute error. The reliability of the proposed MWNNs-GAIPA solver is observed by providing the statistical analysis by using different operators.