Title: Identification of Hammerstein-Wiener time delay model based on approximate least absolute deviation

Authors: Baochang Xu; Zhichao Rong; Yaxin Wang; Likun Yuan

Addresses: Department of Automation, College of Information Science and Engineering, China University of Petroleum (Beijing), China ' Department of Automation, College of Information Science and Engineering, China University of Petroleum (Beijing), China ' Department of Automation, College of Information Science and Engineering, China University of Petroleum (Beijing), China ' Department of Automation, College of Information Science and Engineering, China University of Petroleum (Beijing), China

Abstract: Nonlinearity, time delay and spike noises widely exist in industrial processes. Compared with the linear model, the typical nonlinear Hammerstein-Wiener (H-W) model can describe nonlinear characteristics of industry processes more accurately. In order to overcome the effect of spike noise on the identification results, we propose a stochastic gradient algorithm based on the least absolute deviation in this paper. To solve the non-differentiable problem of the least absolute deviation, an approximate least absolute deviation objective function is established by introducing a deterministic differentiable function to replace the absolute residual. Experiments show the proposed algorithm can suppress the influence of the spike noise on the identification results, and has high identification accuracy and strong robustness.

Keywords: Hammerstein-Wiener model; approximate least absolute deviation; stochastic gradient; spike noise; time delay.

DOI: 10.1504/IJMIC.2023.130119

International Journal of Modelling, Identification and Control, 2023 Vol.42 No.3, pp.251 - 258

Received: 23 Mar 2022
Received in revised form: 12 May 2022
Accepted: 31 May 2022
Published online: 05 Apr 2023 *

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