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System Identification Under Regular, Binary, and Quantized Observations: Moderate Deviations Error Bounds
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DOI：10.1109/TAC.2014.2360022.png)
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Abstract
En 中文
This technical note presents new results on probabilistic characterization of identification errors in their relationships to data sizes and accuracy requirements. Employing the moderate deviations principle, this technical note shows that if the identification accuracy progressively increases with a suitable rate, the probability of an estimate going outside the precision bounds decays exponentially with the data size. The precise rate of the decaying probability is obtained. System identification under regular, binary, and quantized observations are considered. Impact of unmodeled dynamics is also investigated.
Keywords:
Estimation error
identification
moderate deviation
quantized observation
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