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
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D O I：
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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.
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页码：1905 / +
页数：2
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