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On robustness in system identification
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DOI：10.1016/S0005-1098(98)00223-4.png)
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摘要
En 中文
This paper studies robustness issues in system identification. Specifically, a general framework for robust convergence analysis is given for identification of stable linear time-invariant systems. This is used to derive a rather complete set of results on robust convergence under average tb norms, orlicz norms, and cross-correlation-type noise semi-norms. The new theory is used to discuss and to study robustness issues in identification of linear time-invariant models when the true plant is not exactly linear. Robustness issues in system identification against unmodelled nonlinear dynamics are important, as real plants exhibit typically at best nearly linear dynamics. The results show clearly that the effects of unmodelled dynamics are typically more serious than those of random noise. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
noise models
robustness
system identification
worst-case identification
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