Parameter estimation in systems with binary-valued observations and structural uncertainties

This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and senso...

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Published inInternational journal of control Vol. 87; no. 5; pp. 1061 - 1075
Main Authors Kan, Shaobai, Yin, G., Wang, Le Yi
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 04.05.2014
Taylor & Francis Ltd
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ISSN0020-7179
1366-5820
DOI10.1080/00207179.2013.867363

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Abstract This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results.
AbstractList This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results.
This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results. [PUBLICATION ABSTRACT]
Author Wang, Le Yi
Kan, Shaobai
Yin, G.
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Snippet This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement...
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SubjectTerms Algorithms
Bias
binary sensors
Dynamical systems
Empirical analysis
error bounds
Mathematical models
Nonlinear dynamics
nonlinear model mismatch
Nonlinearity
observation bias
Parameter estimation
system identification
Uncertainty
unmodelled dynamics
Title Parameter estimation in systems with binary-valued observations and structural uncertainties
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