On adaptive HMM state estimation

Ford, Jason J. & Moore, John B. (1998) On adaptive HMM state estimation. IEEE Transactions on Signal Processing, 46(2), pp. 475-486.

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In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

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19 citations in Scopus
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15 citations in Web of Science®

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ID Code: 78149
Item Type: Journal Article
Refereed: Yes
DOI: 10.1109/78.655431
ISSN: 1053-587X
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 1998 IEEE
Deposited On: 30 Oct 2014 23:48
Last Modified: 30 Oct 2014 23:48

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