SOLVING A CLASS OF BIOLOGICAL HIV INFECTION MODEL OF LATENTLY INFECTED CELLS USING HEURISTIC APPROACH

The intension of the recent study is to solve a class of biological nonlinear HIV infection model of latently infected CD4+T cells using feed forward artificial neural networks, optimized with global search method, i.e. particle swarm optimization (PSO) and quick local search method, i.e. interior point algorithms (IPA). An unsupervised error function is made based on the differential equations and initial conditions of the HIV infection model represented with latently infected CD4+T cells. For the correctness and reliability of the present scheme, comparison is made of the present results with the Adams numerical results. Moreover, statistical measures based on mean absolute deviation, Theil's inequality coefficient as well as root mean square error demonstrates the effectiveness, applicability and convergence of the designed scheme.

Automated summary

The recent study aims to solve a class of biological nonlinear HIV infection model using feed forward artificial neural networks, optimized with global and local search methods. The comparison with numerical results and statistical measures demonstrate the effectiveness, applicability, and convergence of the designed scheme.