Quantized Identification With Dependent Noise and Fisher Information Ratio of Communication Channels

DOI10.1109/TAC.2009.2039242zbMath1368.94078OpenAlexW2111211976MaRDI QIDQ4978747

G. George Yin, L. Y. Wang

Publication date: 25 August 2017

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tac.2009.2039242

Mathematics Subject Classification ID

Signal detection and filtering (aspects of stochastic processes) (60G35) Identification in stochastic control theory (93E12) Measures of information, entropy (94A17) Statistical aspects of information-theoretic topics (62B10) Channel models (including quantum) in information and communication theory (94A40) Quantum information, communication, networks (quantum-theoretic aspects) (81P45)

Related Items (12)

Robust and nonlinear control literature survey (No. 18) ⋮ Input design in worst-case system identification with quantized measurements ⋮ Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty ⋮ 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 ⋮ Enhanced feedback robustness against communication channel multiplicative uncertainties via scaled dithers ⋮ Four encounters with system identification ⋮ Feedback systems with communications: integrated study of signal estimation, sampling, quantization, and feedback robustness ⋮ Recursive identification of systems with binary-valued outputs and with ARMA noises ⋮ Global convergence of the EM algorithm for ARX models with uncertain communication channels ⋮ Recursive identification of quantized linear systems ⋮ Quantized identification of ARMA systems with colored measurement noise

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