Approximate EM Algorithms for Parameter and State Estimation in Nonlinear Stochastic Models

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 gr...

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Bibliographic Details
Published inProceedings of the 44th IEEE Conference on Decision and Control pp. 368 - 373
Main Authors Goodwin, G.C., Aguero, J.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Availability
Computer industry
Industrial control
Linear systems
Marine vehicles
Mathematical model
Parameter estimation
State estimation
Stochastic processes
Time measurement
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ISBN9780780395671
0780395670
ISSN0191-2216
DOI10.1109/CDC.2005.1582183

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Summary: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.
ISBN:9780780395671
0780395670
ISSN:0191-2216
DOI:10.1109/CDC.2005.1582183