Multiple-prediction-horizon recursive identification of hidden Markov models

Iain B. Collings; John B. Moore

Multiple-prediction-horizon recursive identification of hidden Markov models

Iain B. Collings^*, John B. Moore

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard methods (which do not ensure consistent parameter estimation).

Original language	English
Pages (from-to)	2821-2824
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
Publication status	Published - 1996
Externally published	Yes

Cite this

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title = "Multiple-prediction-horizon recursive identification of hidden Markov models",

abstract = "This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard methods (which do not ensure consistent parameter estimation).",

author = "Collings, {Iain B.} and Moore, {John B.}",

year = "1996",

language = "English",

volume = "5",

pages = "2821--2824",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "0736-7791",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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T1 - Multiple-prediction-horizon recursive identification of hidden Markov models

AU - Collings, Iain B.

AU - Moore, John B.

PY - 1996

Y1 - 1996

N2 - This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard methods (which do not ensure consistent parameter estimation).

AB - This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard methods (which do not ensure consistent parameter estimation).

UR - http://www.scopus.com/inward/record.url?scp=0029750981&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:0029750981

SN - 0736-7791

VL - 5

SP - 2821

EP - 2824

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -

Multiple-prediction-horizon recursive identification of hidden Markov models

Abstract

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