System identification using Hammerstein model

In literature, various linear and nonlinear model structures are defined to identify the systems. Linear models such as Finite Impulse Response (FIR), Infinite Impulse Response (IIR) and Autoregressive (AR) are used in the situations that the input-output relation is signified through linear equivalence. However because of the nonlinear structure of the systems in real life, nonlinear models are developed. Volterra, Bilinear and polynomial autoregressive (PAR) are the examples of nonlinear models. In literature, there are also block oriented models to cascade the linear and nonlinear systems such as Hammerstein, Wiener and Hammerstein Wiener. These models are preferred because of practical use and effective prediction of wide nonlinear process. In this study, system identification applications of Hammerstein model that is cascade of nonlinear Volterra model and linear FIR model. Least mean Square (LMS) and Recursive Least Square (RLS) algorithms are used to identify the Hammerstein model parameters. Furthermore, The results are compared with the FIR model and Volterra model results to identify the success of Hammerstein model.