On Hankel-norm approximation of large-scale Markov chains

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the American Control Conference
PublisherPubl by American Automatic Control Council
Pages1664-1665
Volume2
ISBN (Print)879425652
Publication statusPublished - 1991
Externally publishedYes

Publication series

Name
Volume2
ISSN (Electronic)0743-1619

Conference

Title1991 American Control Conference
PlaceUnited States
CityBoston, MA
Period26 - 28 June 1991

Abstract

A model reduction problem is studied for large-scale Markov chains under an optimal Hankel-norm criterion to establish stable lower-dimensional models in closed form. First, the authors modify a special technique suggested by N. V. Krichagina, R. S. Lipster, and E. Ya. Rubinovich (1985) from one-dimensional to multi-dimensional Markov processes and reformulate a given large-scale Markov chain to a multi-input/multi-output (MIMO) linear time-invariant (LTI) stochastic system. Since the resulting MIMO LTI stochastic system also has a very high dimension, model reduction is necessary for the purpose of performing linear estimation techniques such as the Kalman filtering to obtain an optimal estimate for the given Markov chain.

Citation Format(s)

On Hankel-norm approximation of large-scale Markov chains. / Chen, Guanrong; Chui, Charles K.; Yu, Yaoqi.
Proceedings of the American Control Conference. Vol. 2 Publ by American Automatic Control Council, 1991. p. 1664-1665.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review