Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells

Muhammad Umar, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, J.F. Gómez Aguilar, Fazli Amin and Muhammad Shoaib

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 188, issue C, 241-253

Abstract: In the investigations presented here, an efficient computing approach is applied to solve Human Immunodeficiency Virus (HIV) infection spread. This approach involves CD4+ T-cells by feed-forward artificial neural networks (FF-ANNs) trained with particle swarm optimization (PSO) and interior point method (IPM), i.e., FF-ANN-PSO-IPM. In the proposed solver FF-ANN-PSO-IPM, the FF-ANN models of differential equations are used to develop the fitness functions for an infection model of T-cells. The training of networks through minimization problem are proficiently conducted by integrated heuristic capability of PSO-IPM. The reliability, stability and exactness of the proposed FF-ANN-PSO-IPM are established through comparison with outcomes of standard numerical procedure with Adams method for both single and multiple autonomous trials with precision of order 4 to 8 decimal places of accuracy. The statistical measures are effectively used to validate the outcomes of the proposed FF-ANN-PSO-IPM.

Keywords: HIV infection model; Particle swarm optimization; Artificial neural networks; Interior-point method; Statistical analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:188:y:2021:i:c:p:241-253

DOI: 10.1016/j.matcom.2021.04.008

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