Proceedings Article10.1109/CDC.2007.4434350
System identification using quantized data
Juan C. Agüero,Graham C. Goodwin,Juan I. Yuz +2 more
- 01 Dec 2007
- pp 4263-4268
77
TL;DR: It is argued that, where possible, it is desirable to not utilize "naively" quantized data but instead it is preferable to choose the quantization mechanism carefully, and that using a generalized noise shaping coder improves the accuracy of the estimates.
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Abstract: In this paper we consider the problem of identification of linear systems using quantized data. We argue that, where possible, it is desirable to not utilize "naively" quantized data but instead it is preferable to choose the quantization mechanism carefully. In particular, we show that using a generalized noise shaping coder improves the accuracy of the estimates. We examine the accuracy of estimates for both naive and coded quantizers.
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Citations
Asymptotically efficient parameter estimation using quantized output observations
Le Yi Wang,George Yin +1 more
TL;DR: This paper studies identification of systems in which only quantized output observations are available, and introduces an identification algorithm for system gains that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations.
154
Recursive projection algorithm on FIR system identification with binary-valued observations
Jin Guo,Yanlong Zhao +1 more
TL;DR: A recursive projection algorithm is proposed to estimate the unknown system parameters under some mild conditions on the a priori knowledge of the unknown parameters and inputs and is proved to be convergent in the almost sure and mean square sense.
127
System identification using quantized data
Juan C. Agüero,Graham C. Goodwin,Juan I. Yuz +2 more
- 01 Dec 2007
TL;DR: It is argued that, where possible, it is desirable to not utilize "naively" quantized data but instead it is preferable to choose the quantization mechanism carefully, and that using a generalized noise shaping coder improves the accuracy of the estimates.
77
Consensus Protocol for Multiagent Systems With Undirected Topologies and Binary-Valued Communications
TL;DR: By the two-time-scale protocol, the system is shown to achieve weak consensus and mean square consensus by using a stochastic Lyapunov analysis and the consensus speed is given.
63
Statistical results for system identification based on quantized observations
TL;DR: In this article, the role of adding dithering noise at the sensor is studied, and the overall message is that tailored dithering noises can considerably simplify the derivation of optimal estimators.
62
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Identifiability in dynamic errors‐in‐variables models
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Identifiability of errors in variables dynamic systems
Juan C. Agüero,Graham C. Goodwin +1 more
TL;DR: A single theorem is presented which compactly summarizes many of the known results on errors in variables identifiability problem for dynamic systems and extends the results to a class of multivariable systems.
88