Title: Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm

Authors: Yuzhu Guo; Lingzhong Guo; Stephen A. Billings; Hua-Liang Wei

Addresses: Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK ' Department of Automatic Control and Systems Engineering, and INSIGNEO Institute for in silico Medicine, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK ' Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK ' Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

Abstract: A new iterative orthogonal least squares forward regression (iOFR) algorithm is proposed to identify nonlinear systems which may not be persistently excited. By slightly revising the classic forward orthogonal regression (OFR) algorithm, the new iterative algorithm provides search solutions on a global solution space. Examples show that the new iterative algorithm is computationally efficient and capable of producing a good model even when the input is not completely persistently excited.

Keywords: model structure detection; nonlinear systems; system identification; non-persistence; orthogonal forward regression; iterative OFR; iFOR; iterative learning; non-persistent excitation.

DOI: 10.1504/IJMIC.2015.067496

International Journal of Modelling, Identification and Control, 2015 Vol.23 No.1, pp.1 - 7

Received: 24 Apr 2014
Accepted: 16 May 2014

Published online: 31 Mar 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article