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Identification of Continuous-time Models from Sampled DataRefined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models

Identification of Continuous-time Models from Sampled Data: Refined Instrumental Variable... [This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive noise model is estimated as a discrete-time, autoregressive moving average (ARMA) process. This refined instrumental variable method for continuous-time systems (RIVC) was first developed in 1980 by Young and Jakeman [52] and its simplest embodiment, the simplified RIVC (SRIVC) method, has been used successfully for many years, demonstrating the advantages that this stochastic formulation of the continuous-time estimation problem provides in practical applications (see, e.g., some recent such examples in [16, 34, 40, 45, 48]).] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Identification of Continuous-time Models from Sampled DataRefined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models

Part of the Advances in Industrial Control Book Series
Editors: Garnier, Hugues; Wang, Liuping

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References (51)

Publisher
Springer London
Copyright
© Springer-Verlag London Limited 2008
ISBN
978-1-84800-160-2
Pages
91 –131
DOI
10.1007/978-1-84800-161-9_4
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive noise model is estimated as a discrete-time, autoregressive moving average (ARMA) process. This refined instrumental variable method for continuous-time systems (RIVC) was first developed in 1980 by Young and Jakeman [52] and its simplest embodiment, the simplified RIVC (SRIVC) method, has been used successfully for many years, demonstrating the advantages that this stochastic formulation of the continuous-time estimation problem provides in practical applications (see, e.g., some recent such examples in [16, 34, 40, 45, 48]).]

Published: Jan 1, 2008

Keywords: Noise Model; Global Circulation Model; Transfer Function Model; Monte Carlo Simulation Result; Instrumental Variable Method

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