TS Fuzzy Identification for Mathematical Modeling of HIV Infection

Identifying a system and estimating a model based on Input-Output data is a very important process because many engineering method and solutions depend on the model. In this paper, the problem of nonlinear Takagi-Sugeno (TS) fuzzy system identification of the HIV infection disease is investigated. TS fuzzy system with input-output representation is considered and the parameters of the consequent part are computed based on the least square (LS) and gradient descend (GD) methods. Meanwhile, Gaussian fuzzy membership functions are utilized in the premise part. Furthermore, based on the practical restrictions and response of the HIV disease, a proper pseudorandom binary sequence (PRBS) input signal is proposed. Numerical simulation results show the accuracy of the TS fuzzy model in describing the HIV infectious dynamics.