Recursive Identification Based on Weighted Null-Space Fitting [Elektronisk resurs]
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Fang, Mengyuan (författare)
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IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA
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Galrinho, Miguel (författare)
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Hjalmarsson, Håkan, 1962- (författare)
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KTH Skolan för elektroteknik och datavetenskap (EECS) (utgivare)
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KTH Skolan för elektro- och systemteknik (EES) (utgivare)
- Publicerad: IEEE, 2017
- Engelska.
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Serie: IEEE Conference on Decision and Control, 0743-1546 0743-1546
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Ingår i: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC).
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Sammanfattning
Ämnesord
Stäng
- Algorithms for online system identification consist in updating the estimated model while data are being collected. A standard method for online identification of structured models is the recursive prediction error method (PEM). The problem is that PEM does not have an exact recursive formulation, and the need to rely on approximations makes recursive PEM prone to convergence problems. In this paper, we propose a recursive implementation of weighted null-space fitting, an asymptotically efficient method for identification of structured models. Consisting only of (weighted) least-squares steps, the recursive version of the algorithm has the same convergence and statistical properties of the off-line version. We illustrate these properties with a simulation study, where the proposed algorithm always attains the performance of the off-line version, while recursive PEM often fails to converge.
Ämnesord
- Engineering and Technology (hsv)
- Electrical Engineering, Electronic Engineering, Information Engineering (hsv)
- Teknik och teknologier (hsv)
- Elektroteknik och elektronik (hsv)
Genre
- government publication (marcgt)
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