2020/04/21 by SeyedMehdi Abtahi, Abtahi, SeyedMehdi, Mojtaba Sharifi +1 · 1 citation
Medicine · #FOS: Biological sciences #Hepatitis B Virus Studies #Hepatitis C virus research #Liver Disease Diagnosis and Treatment #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2004.09751
openalex publication_date 2020/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Hepatitis type B is one of the most common infectious disease worldwide that\ncan pose severe threats to human health up to the point that may contribute to\nsevere liver damage or cancer. Over the past two decades a large number of\ndynamic models have been presented based on experimental data to predict the\nHBV infection behavior. Besides, several kinds of controllers have been\nemployed to obtain effective solutions from the HBV treatment. In this essay we\nconsider the nonlinear HBV dynamic model which subjected to both parametric and\nnon-parametric uncertainties without using any linearization. In previous\ncontrol methods three HBV dynamic states should be measured virus safe, and\ninfected cells. However in most of the biological systems, the amount of virus\nis experimentally measured. Accordingly, the necessity to seek a method that\ncan estimate the amount of required drug by receiving the virus data emerges.\nAn ANFIS method is developed in this work to provide an intelligent controller\nfor the drug dosage based on the number of viruses together with an estimator\nfor the amount of infected and uninfected cells. This controller is trained\nfirst using the data provided from a previous adaptive control strategy. After\nthat to improve the closed-loop system capabilities two unmeasured state\nvariables of fundamental dynamics are estimated through the training phase of\nthe ANFIS observer. The results of simulations demonstrated that the accuracy\nof the proposed intelligent controller is high in the tracking of the desired\ndescending virus population.\n