Hasan Zorlu

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.

A comparison study of system identification using Hammerstein model

An attempt has been made in this paper to present performance analysis of a Hammerstein model for system identification area. Hammerstein model block structure is formed by cascade of linear and nonlinear parts. 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 Memoryless Polynomial Nonlinear (MPN) as nonlinear part. In simulations, different systems are identified by proposed Hammerstein model which is optimized with classical and heuristic algorithms. Also, its performance is compared with different models.

An Improved Hammerstein Model for System Identification

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.

Identification of nonlinear systems using Clonal Selection algorithm

In this work, Clonal Selection algorithm (CSA) has been applied to adaptive identification of nonlinear systems and compared its performance to that of Genetic algorithm (GA). Nonlinear Box-Jenkins system which is frequently used as a benchmark example for testing in literature and a parametric nonlinear bilinear system have been identified using these algorithms. The simulation results have shown that nonlinear systems can be identified using CSA with low modeling error.

A new multi volterra system model based on artificial neural network for nonlinear system identification

In this paper, a new multi Volterra system model based on artificial neural network for nonlinear system identification is proposed. In this work, our attention is especially focused on behaviours of system which aren’t suffer from noise added to input and produce desired output. The proposed model were tested with Gaussian distribution noise signals at different levels and compared with single Volterra systems. The proposed method is illustrated by simulations. These simulations indicate that the performance of proposed model is better than single Volterra systems.