Multiple-prediction-horizon recursive identification of hidden Markov models

Author

Collings, Iain B. ; Moore, John B.

Author_Institution

Coop Res. Centre for Sensor Signal & Inf. Process., Univ. of South Australia, The Levels, SA, Australia

Volume

5

fYear

1996

fDate

7-10 May 1996

Firstpage

2821

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)

Keywords

error analysis; hidden Markov models; parameter estimation; prediction theory; recursive estimation; signal processing; state-space methods; excitation conditions; hidden Markov models; multiple prediction horizon; online identification; parameter estimation; recursive identification; recursive prediction error; simulation studies; state space signal model; Biomedical signal processing; Equations; Hidden Markov models; Information processing; Parameter estimation; Sensor systems; Signal processing; Signal processing algorithms; State-space methods; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.550140

Filename

550140