System Identification Using Regular and Quantized Observations

Applications of Large Deviations Principles

  • Qi He
  • Le Yi Wang
  • G. George Yin

Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Qi He, Le Yi Wang, G. George Yin
    Pages 1-7
  3. Qi He, Le Yi Wang, G. George Yin
    Pages 9-10
  4. Qi He, Le Yi Wang, G. George Yin
    Pages 11-15
  5. Qi He, Le Yi Wang, G. George Yin
    Pages 37-42
  6. Qi He, Le Yi Wang, G. George Yin
    Pages 43-53
  7. Qi He, Le Yi Wang, G. George Yin
    Pages 55-69
  8. Qi He, Le Yi Wang, G. George Yin
    Pages 71-80
  9. Qi He, Le Yi Wang, G. George Yin
    Pages 81-87
  10. Back Matter
    Pages 89-95

About this book

Introduction

​This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular.  By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.

Keywords

System identification binary observation error estimate large deviations parameter estimation quantized observation

Authors and affiliations

  • Qi He
    • 1
  • Le Yi Wang
    • 2
  • G. George Yin
    • 3
  1. 1., Department of MathematicsUniversity of CaliforniaIrvineUSA
  2. 2., Department of Electrical & Computer EngWayne State UniversityDetroitUSA
  3. 3., Department of MathematicsWayne State UniversityDetroitUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-6292-7
  • Copyright Information Qi He, Le Yi Wang, and G. George Yin 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-6291-0
  • Online ISBN 978-1-4614-6292-7
  • Series Print ISSN 2191-8198
  • Series Online ISSN 2191-8201
  • About this book
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