- Title
- Estimating state-space models in innovations form using the expectation maximisation algorithm
- Creator
- Wills, Adrian; Schön, Thomas B.; Ninness, Brett
- Relation
- 49th IEEE Conference on Decision and Control (CDC 2010). Proceedings of the 49th IEEE Conference on Decision and Control (Atlanta, GA 15-17 December, 2010) p. 5524-5529
- Publisher Link
- http://dx.doi.org/10.1109/CDC.2010.5717145
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2010
- Description
- The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.
- Subject
- EM algorithm; expectation maximisation; missing data; state-space models; algorithms
- Identifier
- http://hdl.handle.net/1959.13/933201
- Identifier
- uon:11565
- Identifier
- ISBN:9781424477463
- Rights
- Copyright © 2010 IEEE. Reprinted from Proceedings of the 49th IEEE Conference on Decision and Control p. 5524-5529. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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