An iterative algorithm for simulation error based identification of polynomial input-output models using multi-step prediction

Effective identification of polynomial input-output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advan...

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Bibliographic Details
Published in:International journal of control Vol. 83; no. 7; pp. 1442 - 1456
Main Authors: Farina, Marcello, Piroddi, Luigi
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis Group 01.07.2010
Taylor & Francis
Taylor & Francis Ltd
Subjects:
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Mathematical models
Modelling and identification
nonlinear model identification
output error identification
Parameter estimation
prediction error minimisation methods
simulation error minimisation methods
Studies
Short term
Capability index
Non linear estimation
Parameter estimation
Iterative method
System identification
Polynomial method
Modeling
Input output model
ISSN:0020-7179, 1366-5820
Online Access:Get full text
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Summary:Effective identification of polynomial input-output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input-output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0020-7179
1366-5820
DOI:10.1080/00207171003793262