On convergence of a BCLS algorithm for noisy autoregressive process estimation

Chun Zhi Jin; Li Juan Jia; Zi Jiang Yang; Kiyoshi Wada

On convergence of a BCLS algorithm for noisy autoregressive process estimation

Chun Zhi Jin^*, Li Juan Jia, Zi Jiang Yang, Kiyoshi Wada

^*Corresponding author for this work

Kyushu University

Research output: Contribution to journal › Conference article › peer-review

9 Citations (Scopus)

Abstract

The identification of AR processes whose measurements are corrupted by additive noise is considered. A bias compensated least squares (BCLS) algorithm is derived on the framework of solving nonlinear bias compensation equation (BCE). The framework is convenience for investigating the convergence property of the algorithm. Convergence analysis of the proposed algorithm is performed from the numerical analysis viewpoint. The algorithm is to find a fixed point of the BCE. By examination of the BCE and their Jacobian, a theoretical result is obtained to make clear that the relationship of convergence and the parameters of the AR processes as well as the ratio of noise to signal. Based on the results of convergence analysis, it can be expected that more effective estimation algorithms are developed.

Original language	English
Pages (from-to)	4252-4257
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	4
Publication status	Published - 2002
Externally published	Yes
Event	41st IEEE Conference on Decision and Control - Las Vegas, NV, United States Duration: 10 Dec 2002 → 13 Dec 2002

Cite this

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title = "On convergence of a BCLS algorithm for noisy autoregressive process estimation",

abstract = "The identification of AR processes whose measurements are corrupted by additive noise is considered. A bias compensated least squares (BCLS) algorithm is derived on the framework of solving nonlinear bias compensation equation (BCE). The framework is convenience for investigating the convergence property of the algorithm. Convergence analysis of the proposed algorithm is performed from the numerical analysis viewpoint. The algorithm is to find a fixed point of the BCE. By examination of the BCE and their Jacobian, a theoretical result is obtained to make clear that the relationship of convergence and the parameters of the AR processes as well as the ratio of noise to signal. Based on the results of convergence analysis, it can be expected that more effective estimation algorithms are developed.",

author = "Jin, {Chun Zhi} and Jia, {Li Juan} and Yang, {Zi Jiang} and Kiyoshi Wada",

year = "2002",

language = "English",

volume = "4",

pages = "4252--4257",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0743-1546",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "41st IEEE Conference on Decision and Control ; Conference date: 10-12-2002 Through 13-12-2002",

}

TY - JOUR

T1 - On convergence of a BCLS algorithm for noisy autoregressive process estimation

AU - Jin, Chun Zhi

AU - Jia, Li Juan

AU - Yang, Zi Jiang

AU - Wada, Kiyoshi

PY - 2002

Y1 - 2002

N2 - The identification of AR processes whose measurements are corrupted by additive noise is considered. A bias compensated least squares (BCLS) algorithm is derived on the framework of solving nonlinear bias compensation equation (BCE). The framework is convenience for investigating the convergence property of the algorithm. Convergence analysis of the proposed algorithm is performed from the numerical analysis viewpoint. The algorithm is to find a fixed point of the BCE. By examination of the BCE and their Jacobian, a theoretical result is obtained to make clear that the relationship of convergence and the parameters of the AR processes as well as the ratio of noise to signal. Based on the results of convergence analysis, it can be expected that more effective estimation algorithms are developed.

AB - The identification of AR processes whose measurements are corrupted by additive noise is considered. A bias compensated least squares (BCLS) algorithm is derived on the framework of solving nonlinear bias compensation equation (BCE). The framework is convenience for investigating the convergence property of the algorithm. Convergence analysis of the proposed algorithm is performed from the numerical analysis viewpoint. The algorithm is to find a fixed point of the BCE. By examination of the BCE and their Jacobian, a theoretical result is obtained to make clear that the relationship of convergence and the parameters of the AR processes as well as the ratio of noise to signal. Based on the results of convergence analysis, it can be expected that more effective estimation algorithms are developed.

UR - http://www.scopus.com/inward/record.url?scp=0036991315&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0036991315

SN - 0743-1546

VL - 4

SP - 4252

EP - 4257

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - 41st IEEE Conference on Decision and Control

Y2 - 10 December 2002 through 13 December 2002

ER -

On convergence of a BCLS algorithm for noisy autoregressive process estimation

Abstract

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