Continuous-time model identification from noisy input/output measurements using fourth-order cumulants

被引：0

作者：

Thil, Stephane ^{[1
]}

Garnier, Hugues ^{[1
]}

Gilson, Marion ^{[1
]}

Mahata, Kaushik ^{[2
]}

机构：

[1] Nancy Univ, Ctr Rech Automat Nancy, CNRS, Fac Sci & Tech, BP 239, F-54506 Vandoeuvre Les Nancy, France

[2] Univ Newcastle, Ctr Complex Dynam Syst & Control, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia

来源：

PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14 | 2007年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to be non-Gaussian, whereas the noises contaminating the data are assumed to be Gaussian. The fourth-order cumulants of the input/output data are then (asymptotically) insensitive to the noises, that can be coloured and/or mutually correlated. Two estimators based on this noise-cancellation property are proposed. The performance of the proposed algorithms are assessed through a numerical simulation.

引用

页码：1905 / +

页数：2

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