Reduced complexity on-line estimation of hidden Markov model parameters

Moore, John B. & Ford, Jason J. (1998) Reduced complexity on-line estimation of hidden Markov model parameters. In Proceedings of the 1998 International Conference on Optimization: Techniques and Applications.


In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

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ID Code: 78152
Item Type: Conference Paper
Refereed: Yes
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 [please consult the authors]
Deposited On: 30 Oct 2014 22:58
Last Modified: 30 Oct 2014 22:58

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