Almost sure convergence rates for system identification using binary, quantized, and regular sensors

DOI10.1016/j.automatica.2014.05.036zbMath1297.93169OpenAlexW2080664515MaRDI QIDQ466294

Hongwei Mei, George Yin, L. Y. Wang

Publication date: 24 October 2014

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2014.05.036

zbMATH Keywords

system identificationalmost sure convergence ratelaws of iterated logarithmsquantized sensor

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12)

Related Items

Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs ⋮ Recursive identification of FIR systems with binary-valued outputs and communication channels ⋮ FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs ⋮ Recursive identification of systems with binary-valued outputs and with ARMA noises ⋮ Gradient-based parameter identification algorithms for observer canonical state space systems using state estimates ⋮ Recursive identification of quantized linear systems ⋮ Quantized identification of ARMA systems with colored measurement noise

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