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System Identification Using Regular and Quantized Observations

Applications of Large Deviations Principles

  • Book
  • © 2013

Overview

Authors:
  1. Qi He

    1. , Department of Mathematics, University of California, Irvine, USA

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  2. Le Yi Wang

    1. , Department of Electrical & Computer Eng, Wayne State University, Detroit, USA

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    You can also search for this author in PubMed   Google Scholar

  3. G. George Yin

    1. , Department of Mathematics, Wayne State University, Detroit, USA

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  • Presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular
  • First book devoted to large deviations to system identification
  • Application oriented

Part of the book series: SpringerBriefs in Mathematics (BRIEFSMATH)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction and Overview

    • Qi He, Le Yi Wang, G. George Yin
    Pages 1-7

  3. System Identification: Formulation

    • Qi He, Le Yi Wang, G. George Yin
    Pages 9-10

  4. Large Deviations: An Introduction

    • Qi He, Le Yi Wang, G. George Yin
    Pages 11-15

  5. LDP of System Identification under Independent and Identically Distributed Observation Noises

    • Qi He, Le Yi Wang, G. George Yin
    Pages 17-35

  6. LDP of System Identification under Mixing Observation Noises

    • Qi He, Le Yi Wang, G. George Yin
    Pages 37-42

  7. Applications to Battery Diagnosis

    • Qi He, Le Yi Wang, G. George Yin
    Pages 43-53

  8. Applications to Medical Signal Processing

    • Qi He, Le Yi Wang, G. George Yin
    Pages 55-69

  9. Applications to Electric Machines

    • Qi He, Le Yi Wang, G. George Yin
    Pages 71-80

  10. Remarks and Conclusion

    • Qi He, Le Yi Wang, G. George Yin
    Pages 81-87

  11. Back Matter

    Pages 89-95
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Keywords

About this book

​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.

Authors and Affiliations

  • , Department of Mathematics, University of California, Irvine, USA

    Qi He

  • , Department of Electrical & Computer Eng, Wayne State University, Detroit, USA

    Le Yi Wang

  • , Department of Mathematics, Wayne State University, Detroit, USA

    G. George Yin

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