Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
by Yuzhu Guo; Lingzhong Guo; Stephen A. Billings; Hua-Liang Wei
International Journal of Modelling, Identification and Control (IJMIC), Vol. 23, No. 1, 2015

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.

Online publication date: Tue, 31-Mar-2015

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