Comparison of Prediction-Error-Modelling Criteria

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Abstract

Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest to use the one-step-ahead prediction-error maximum-likelihood (or maximum a posteriori) estimator. It gives consistent estimates of all parameters and the parameter estimates are almost identical to the estimates obtained for long prediction horizons but with consumption of significantly less computational resources. The identification method is suitable for predictive control.
Original languageEnglish
Title of host publicationAmerican Control Conference, 2007. ACC '07
PublisherIEEE
Publication date2007
ISBN (Print)1-4244-0988-8
DOIs
Publication statusPublished - 2007
EventAmerican Control Conference 2007 - New York City, United States
Duration: 11 Jul 200713 Jul 2007
http://a2c2.org/conferences/acc2007/

Conference

ConferenceAmerican Control Conference 2007
CountryUnited States
CityNew York City
Period11/07/200713/07/2007
Internet address

Bibliographical note

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