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System Identification with Quantized Observations

  • Book
  • © 2010

Overview

Authors:
  1. Le Yi Wang

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

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  2. G. George Yin

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

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  3. Ji-Feng Zhang

    1. Academy of Mathematics & Systems Sci., Inst. Systems Science, Chinese Academy of Sciences, Beijing, China, People's Republic

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    You can also search for this author in PubMed   Google Scholar

  4. Yanlong Zhao

    1. Academy of Mathematics & Systems Sci., Inst. Systems Science, Chinese Academy of Sciences, Beijing, China, People's Republic

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    You can also search for this author in PubMed   Google Scholar

  • First monograph dedicated to quantized identification in systems
  • Applications to communication and computer networks, signal processing, sensor networks, mobile agents, data fusion, remote sensing, telemedicine
  • Selected material from the book may be used in graduate-level courses on system identification
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Overview

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 3-11

    3. System Settings

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 13-22

  3. Stochastic Methods for Linear Systems

    1. Front Matter

      Pages 23-23
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    2. Empirical-Measure-Based Identification: Binary-Valued Observations

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 25-47

    3. Estimation Error Bounds: Including Unmodeled Dynamics

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 49-57

    4. Rational Systems

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 59-66

    5. Quantized Identification and Asymptotic Efficiency

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 67-79

    6. Input Design for Identification in Connected Systems

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 81-93

    7. Identification of Sensor Thresholds and Noise Distribution Functions

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 95-116

  4. Deterministic Methods for Linear Systems

    1. Front Matter

      Pages 117-117
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    2. Worst-Case Identification under Binary-Valued Observations

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 119-147

    3. Worst-Case Identification Using Quantized Observations

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 149-169

  5. Identification of Nonlinear and Switching Systems

    1. Front Matter

      Pages 171-171
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    2. Identification of Wiener Systems with Binary-Valued Observations

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 173-195

    3. Identification of Hammerstein Systems with Quantized Observations

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 197-223

    4. Systems with Markovian Parameters

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 225-252

  6. Complexity Analysis

    1. Front Matter

      Pages 253-253
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    2. Space and Time Complexities, Threshold Selection, Adaptation

      • Le Yi Wang, G. George Yin, Ji-Feng Zhang, Yanlong Zhao
      Pages 255-273
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Keywords

About this book

This book concerns the identi?cation of systems in which only quantized output observations are available, due to sensor limitations, signal quan- zation, or coding for communications. Although there are many excellent treaties in system identi?cation and its related subject areas, a syst- atic study of identi?cation with quantized data is still in its early stage. This book presents new methodologies that utilize quantized information in system identi?cation and explores their potential in extending control capabilities for systems with limited sensor information or networked s- tems. The book is an outgrowth of our recent research on quantized iden- ?cation; it o?ers several salient features. From the viewpoint of targeted plants, it treats both linear and nonlinear systems, and both time-invariant and time-varying systems. In terms of noise types, it includes independent and dependent noises, stochastic disturbances and deterministic bounded noises, and noises with unknown distribution functions. The key meth- ologies of the book combine empirical measures and information-theoretic approaches to cover convergence, convergence rate, estimator e?ciency, - put design, threshold selection, and complexity analysis. We hope that it can shed new insights and perspectives for system identi?cation.

Reviews

From the reviews:

“The central idea in this book is to provide a comprehensive treatment of both theory and algorithms needed for parameter identification of systems with quantized observations. … the book conveys a clear and very complete overview of recent exciting developments in the area of identification with quantized observations. It is meant as a ‘state-of-the-art’ book … . All this makes the book an extremely valuable resource for researchers and engineers interested in modern system identification.” (Dariusz Uciński, Mathematical Reviews, Issue 2011 i)

Authors and Affiliations

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

    Le Yi Wang

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

    G. George Yin

  • Academy of Mathematics & Systems Sci., Inst. Systems Science, Chinese Academy of Sciences, Beijing, China, People's Republic

    Ji-Feng Zhang, Yanlong Zhao

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