Empirical-Measure-Based Identification: Binary-Valued Observations

  • Le Yi Wang
  • G. George Yin
  • Ji-Feng Zhang
  • Yanlong Zhao
Part of the Systems & Control: Foundations & Applications book series (SCFA)


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.


Brownian Motion Weak Convergence Strong Convergence Asymptotic Distribution Empirical Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Le Yi Wang
    • 1
  • G. George Yin
    • 2
  • Ji-Feng Zhang
    • 3
  • Yanlong Zhao
    • 3
  1. 1.Department of Electrical and Computer EngineeringWayne State UniversityDetroitUSA
  2. 2.Department of MathematicsWayne State UniversityDetroitUSA
  3. 3.Key Laboratory of Systems and Control, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina

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