- Title
- Approximate EM algorithms for parameter and state estimation in nonlinear stochastic models
- Creator
- Goodwin, Graham C.; Aguero, Juan C.
- Relation
- CDC-ECC '05: 44th IEEE Conference on Decision and Control, 2005 and European Control Conference 2005. . Proceedings of 44th IEEE Conference on Decision and Control and European Control Conference 2005 (Seville, Spain 12-15 December, 2005 ) p. 368-373
- Relation
- http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1582183
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2005
- Description
- Due to the availability of rapidly improving computer speeds, industry is increasingly using nonlinear process models in calculations that appear further down the control hierarchy. Indeed, nonlinear models are now frequently used for real-time control calculations. This trend means that there is growing interest in the availability of high speed state and parameter estimation algorithms for nonlinear models. One family of algorithms that can be used for this purpose is based on the, so called, Expectation Maximization Scheme. Unfortunately, in its basic form, this algorithm requires large computational resources. In this paper we review the EM algorithm and propose several approximate schemes aimed at retaining the essential flavour of this class of algorithm whilst ensuring that the computations are tractable. We will also compare the EM algorithm with several simpler schemes via a number of examples and comment on the trade-offs that occur.
- Subject
- nonlinear process models; parameter estimation algorithms; Expectation Maximization Scheme
- Identifier
- http://hdl.handle.net/1959.13/28633
- Identifier
- uon:2158
- Identifier
- ISBN:0780395670
- Rights
- Copyright © 2005 IEEE. Reprinted from the 44th IEEE Conference on Decision and Control 2005 and European Control Conference 2005, p. 368-373. 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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