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Identification of non-linear stochastic systems by state dependent parameter estimation.

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Identification of non-linear stochastic systems by state dependent parameter estimation. / Young, Peter C.; McKenna, Paul; Bruun, John.
In: International Journal of Control, Vol. 74, No. 18, 12.2001, p. 1837-1857.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Young, PC, McKenna, P & Bruun, J 2001, 'Identification of non-linear stochastic systems by state dependent parameter estimation.', International Journal of Control, vol. 74, no. 18, pp. 1837-1857. https://doi.org/10.1080/00207170110089824

APA

Young, P. C., McKenna, P., & Bruun, J. (2001). Identification of non-linear stochastic systems by state dependent parameter estimation. International Journal of Control, 74(18), 1837-1857. https://doi.org/10.1080/00207170110089824

Vancouver

Young PC, McKenna P, Bruun J. Identification of non-linear stochastic systems by state dependent parameter estimation. International Journal of Control. 2001 Dec;74(18):1837-1857. doi: 10.1080/00207170110089824

Author

Young, Peter C. ; McKenna, Paul ; Bruun, John. / Identification of non-linear stochastic systems by state dependent parameter estimation. In: International Journal of Control. 2001 ; Vol. 74, No. 18. pp. 1837-1857.

Bibtex

@article{f8d2306c70e04a9c9b8131b7743aa667,
title = "Identification of non-linear stochastic systems by state dependent parameter estimation.",
abstract = "The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.",
author = "Young, {Peter C.} and Paul McKenna and John Bruun",
year = "2001",
month = dec,
doi = "10.1080/00207170110089824",
language = "English",
volume = "74",
pages = "1837--1857",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",
number = "18",

}

RIS

TY - JOUR

T1 - Identification of non-linear stochastic systems by state dependent parameter estimation.

AU - Young, Peter C.

AU - McKenna, Paul

AU - Bruun, John

PY - 2001/12

Y1 - 2001/12

N2 - The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.

AB - The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.

U2 - 10.1080/00207170110089824

DO - 10.1080/00207170110089824

M3 - Journal article

VL - 74

SP - 1837

EP - 1857

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

IS - 18

ER -