System Identification Using Regular and Quantized Observations

DOI10.1007/978-1-4614-6292-7zbMath1285.93003OpenAlexW2496655573MaRDI QIDQ4902301

Qi He, G. George Yin, L. Y. Wang

Publication date: 14 January 2013

Published in: SpringerBriefs in Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4614-6292-7

zbMATH Keywords

system identificationlarge deviation estimatesapplications of large deviation principlesmixing-type noisesquantized sensorsregular sensors

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics in engineering and industry; control charts (62P30) Research exposition (monographs, survey articles) pertaining to statistics (62-02) Research exposition (monographs, survey articles) pertaining to systems and control theory (93-02) Identification in stochastic control theory (93E12)

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Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs ⋮ FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs ⋮ Robust noise attenuation under stochastic noises and worst-case unmodelled dynamics ⋮ Parameter estimation in systems with binary-valued observations and structural uncertainties

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