Auxiliary variable-based identification algorithms for uncertain-input models

Chen, Jing; Zhu, Quanmin; Chandra, Budi; Pu, Yan

Abstract

This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive least squares algorithm is proposed to reduce the computational efforts. To validate the framework and algorithms developed, it has conducted a series of bench tests with computational experiments. The simulated numerical results/plots are consistent with the analytically derived results in terms of the feasibility and effectiveness of the proposed procedure.

Citation

Chen, J., Zhu, Q., Chandra, B., & Pu, Y. (2020). Auxiliary variable-based identification algorithms for uncertain-input models. Circuits, Systems, and Signal Processing, 39, 3389-3404. https://doi.org/10.1007/s00034-019-01320-w

• Journal Article Type: Article
• Acceptance Date: Nov 29, 2019
• Online Publication Date: Dec 7, 2019
• Publication Date: 2020-07
• Deposit Date: Mar 13, 2020
• Journal: Circuits, Systems, and Signal Processing
• Print ISSN: 0278-081X
• Electronic ISSN: 1531-5878
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 39
• Pages: 3389-3404
• DOI: https://doi.org/10.1007/s00034-019-01320-w
• Keywords: Signal Processing; Applied Mathematics
• Public URL: https://uwe-repository.worktribe.com/output/5667735
• Additional Information: Received: 2 July 2019; Revised: 28 November 2019; Accepted: 29 November 2019; First Online: 7 December 2019