A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells

• DOI: 10.1016/j.rinp.2021.104235
• Publication Date: 2021-05-01T08:53:21Z
• Authors: Muhammad Umar; Zulqurnain Sabir; Muhammad Asif Zah...; Haci Mehmet Baskonus; Shao-Wen Yao; Esin Ilhan
• Keywords: Sequential quadratic programming; Solver; Morlet wavelet

The aim of this study is to provide the numerical outcomes a nonlinear HIV infection system latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search genetic algorithms (GAs) hybridized for speedy local sequential quadratic programming (SQP), i.e., MW-ANN-GA-SQP. design an error function presented by designing MW-ANN models differential equations along initial conditions that represent involving cells. precision and persistence approach MW-ANN-GA-SQP are recognized through comparative studies from results Runge-Kutta scheme solving spread case single multiple trails Statistical estimates 'Theil's inequality coefficient' 'root mean square error' based indices further validate sustainability applicability proposed solver.