An Improved Hammerstein Model for System Identification

Abstract

System identification can easily model practical applications such as peer to peer (P2P) file-sharing traffic, driver assistance system, road traffic state, ethernet-based traffic flows. Therefore, system identification process can also be used in the smart city concept. This paper aims to improve Hammerstein model for system identification area. Hammerstein model block structure is formed by cascade of linear and nonlinear parts. Generally, MPN (Memoryless Polynomial Nonlinear) model for nonlinear part and FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) model for linear part are preferred in Hammerstein models in literature. This study different from the studies in literature, focuses on the performance of Hammerstein block model that Second Order Volterra (SOV) Model is preferred instead of MPN as nonlinear part. In this context, a SOV based Hammerstein model structure is presented. In simulations, different nonlinear and linear systems are identified by our proposed Hammerstein model which is optimized with classical and heuristic algorithms. Also, its performance is compared with different linear and nonlinear models. We believe, we are the first ones to study and compare the performance between SOV based Hammerstein model and MPN based Hammerstein model. The main benefit of this study is that simulation results reveal the effectiveness and robustness of the proposed model. Therefore, Hammerstein model may be preferred to model different type of system in smart cities.