Title: Identification of Hammerstein-Wiener non-linear dynamic models using conjugate gradient-based iterative algorithm

Authors: Xiangli Li; Lincheng Zhou

Addresses: School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, Jiangsu, China ' School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, Jiangsu, China

Abstract: This paper mainly studies the identification of a class of nonlinear dynamic models with Hammerstein-Wiener non-linearity. Firstly, a special form of Hammerstein-Wiener polynomial model is constructed by using the key term decomposition technique to separate the model parameters to be estimated. On this basis, an iterative algorithm based on Conjugate Gradient (CGI) is proposed, which computes a new conjugate vector along the conjugate direction in each iteration step. Because the search direction of the CGI algorithm is conjugate with respect to the Hessian matrix of the cost function, the CGI algorithm can generally obtain the faster convergence rates than the gradient-based iterative algorithm. By conjugating the search direction of the CGI algorithm with the Hessian matrix of the loss function, CGI algorithm has more advantages in convergence rates than the gradient-based iterative algorithm. Finally, numerical examples are given to demonstrate the effectiveness of the proposed algorithm.

Keywords: Hammerstein-Wiener model; conjugate gradient; key term decomposition; Hessian matrix; parameter estimation.

DOI: 10.1504/IJCAT.2021.121521

International Journal of Computer Applications in Technology, 2021 Vol.67 No.2/3, pp.111 - 118

Received: 13 Oct 2020
Accepted: 25 Nov 2020
Published online: 17 Mar 2022 *

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