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System Identification with Quantized Observations pp 25–47Cite as

Empirical-Measure-Based Identification: Binary-Valued Observations

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Part of the book series: Systems & Control: Foundations & Applications ((SCFA))

Abstract

This chapter presents a stochastic framework for systems identification based on empirical measures that are derived from binary-valued observations. This scenario serves as a fundamental building block for subsequent studies on quantized observation data.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, 48202, USA

    Le Yi Wang

  2. Department of Mathematics, Wayne State University, Detroit, MI, 48202, USA

    G. George Yin

  3. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Ji-Feng Zhang & Yanlong Zhao

Authors
  1. Le Yi Wang
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  2. G. George Yin
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  3. Ji-Feng Zhang
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  4. Yanlong Zhao
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Corresponding author

Correspondence to Le Yi Wang .

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Wang, L.Y., Yin, G.G., Zhang, JF., Zhao, Y. (2010). Empirical-Measure-Based Identification: Binary-Valued Observations. In: System Identification with Quantized Observations. Systems & Control: Foundations & Applications. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4956-2_3

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