A linear programming approach to online set membership parameter estimation for linear regression models

Summary This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown‐but‐bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing 2017-03, Vol.31 (3), p.360-378
Main Authors: Casini, M., Garulli, A., Vicino, A.
Format: Article
Language:English
Subjects:
Approximation
Binding
Linear programming
Mathematical analysis
Mathematical models
Optimization
Parameter estimation
recursive identification
Regression
set approximation
set membership identification
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Summary:Summary This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown‐but‐bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a suitable linear program, whenever a new measurement leads to a reduction of the approximating orthotope. The key idea for preventing the size of the linear programs from steadily increasing is to propagate only the binding constraints of these optimization problems. Numerical studies show that the new approach outperforms existing recursive set approximation techniques, while keeping the required computational burden within the same order of magnitude. Copyright © 2016 John Wiley & Sons, Ltd.
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.2701