Quantized Identification and Asymptotic Efficiency

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


Up to this point, we have been treating binary-valued observations. The fundamental principles and basic algorithms for binary-valued observations can be modified to handle quantized observations as well. One way to understand the connection is to view a quantized observation as a vector-valued binary observation in which each vector component represents the output of one threshold, which is a binary-valued sensor. The dimension of the vector is the number of the thresholds in the quantized sensor.


Unbiased Estimator Quantization Error Uniform Quantization Minimum Variance Unbiased Estimator Rational Transfer Function 
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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