Description

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors’ research provides a base for the book, but it incorporates the results from the latest international research publications.

Key Features

  • Named a 2013 Notable Computer Book for Information Systems by Computing Reviews
  • One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments
  • Contains numerous illustrative examples to help the reader grasp basic methods

Readership

Engineers, scientists and graduate students interested in information theory, signal processing, system identification and adaptive system training.

Table of Contents

  • About the Authors
  • Preface
  • Symbols and Abbreviations
  • 1. Introduction
    • 1.1 Elements of System Identification
    • 1.2 Traditional Identification Criteria
    • 1.3 Information Theoretic Criteria
    • 1.4 Organization of This Book
    • Appendix A Unifying Framework of ITL
  • 2. Information Measures
    • 2.1 Entropy
    • 2.2 Mutual Information
    • 2.3 Information Divergence
    • 2.4 Fisher Information
    • 2.5 Information Rate
    • Appendix B -Stable Distribution
    • Appendix C Proof of (2.17)
    • Appendix D Proof of Cramer–Rao Inequality
  • 3. Information Theoretic Parameter Estimation
    • 3.1 Traditional Methods for Parameter Estimation
    • 3.2 Information Theoretic Approaches to Classical Estimation
    • 3.3 Information Theoretic Approaches to Bayes Estimation
    • 3.4 Information Criteria for Model Selection
    • Appendix E: EM Algorithm
    • Appendix F: Minimum MSE Estimation
    • Appendix G: Derivation of AIC Criterion
  • 4. System Identification Under Minimum Error Entropy Criteria
    • 4.1 Brief Sketch of System Parameter Identification
    • 4.2 MEE Identification Criterion
    • 4.3 Identification Algorithms Under MEE Criterion
    • 4.4 Convergence Analysis
    • 4.5 Optimization of -Entropy Criterion
    • 4.6 Survival Information Potential Criterion
    • 4.7 Δ-Entropy Criterion
    • 4.8 System Identification with MCC
    • Appendix H Vector Gradient and Matrix Gradient
  • 5. System Identification Under Information Divergence Criteria
    • 5.1 Parameter Identifiability Under KLID Criterion
    • 5.2 Minimum Information Divergence Identification with Reference PDF
  • 6. System Identification Based on Mutual Information Criteria
    • 6.1 System Identification Under the

Details

No. of pages:
266
Language:
English
Copyright:
© 2013
Published:
Imprint:
Elsevier
Print ISBN:
9780124045743
Electronic ISBN:
9780124045958

Awards

Notable Computing Books 2013: Information Systems, Computing Reviews

Reviews

"…almost all of the variables used in the formulas are defined, something I cannot say about many other mathematical books…I found this book timely, interesting, and very well written. Readers can learn about estimation methodologies, the art of proof, and identification of the parameters assumed by the system architect or designer."--ComputingReviews.com, March 5, 2014
"Chen… Zhu, Hu…and Principe…synthesize their recent papers into a single-volume reference on system identification under criteria based on the information theory descriptors of entropy and dissimilarity. They cover information measures, information theoretic parameter estimation, system identification under minimum error entropy criteria, system identification under information divergence criteria, and system identification based on mutual information criteria."--Reference & Research Book News, December 2013