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Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB

Book Description

Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology.

After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis.

  • Covers all contemporary main methods for classification and estimation.

  • Integrated approach to classification, parameter estimation and state estimation

  • Highlights the practical deployment of theoretical issues.

  • Provides a concise and practical approach supported by MATLAB toolbox.

  • Offers exercises at the end of each chapter and numerous worked out examples.

  • PRtools toolbox (MATLAB) and code of worked out examples available from the internet

  • Many examples showing implementations in MATLAB

  • Enables students to practice their skills using a MATLAB environment

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. Foreword
  7. Chapter 1: Introduction
    1. 1.1 THE SCOPE OF THE BOOK
    2. 1.2 ENGINEERING
    3. 1.3 THE ORGANIZATION OF THE BOOK
    4. 1.4 REFERENCES
  8. Chapter 2: Detection and Classification
    1. 2.1 BAYESIAN CLASSIFICATION
    2. 2.2 REJECTION
    3. 2.3 DETECTION: THE TWO-CLASS CASE
    4. 2.4 SELECTED BIBLIOGRAPHY
    5. 2.5 EXERCISES
  9. Chapter 3: Parameter Estimation
    1. 3.1 BAYESIAN ESTIMATION
    2. 3.2 PERFORMANCE OF ESTIMATORS
    3. 3.3 DATA FITTING
    4. 3.4 OVERVIEW OF THE FAMILY OF ESTIMATORS
    5. 3.5 SELECTED BIBLIOGRAPHY
    6. 3.6 EXERCISES
  10. Chapter 4: State Estimation
    1. 4.1 A GENERAL FRAMEWORK FOR ONLINE ESTIMATION
    2. 4.2 CONTINUOUS STATE VARIABLES
    3. 4.3 DISCRETE STATE VARIABLES
    4. 4.4 MIXED STATES AND THE PARTICLE FILTER
    5. 4.5 SELECTED BIBLIOGRAPHY
    6. 4.6 EXERCISES
  11. Chapter 5: Supervised Learning
    1. 5.1 TRAINING SETS
    2. 5.2 PARAMETRIC LEARNING
    3. 5.3 NONPARAMETRIC LEARNING
    4. 5.4 EMPIRICAL EVALUATION
    5. 5.5 REFERENCES
    6. 5.6 EXERCISES
  12. Chapter 6: Feature Extraction and Selection
    1. 6.1 CRITERIA FOR SELECTION AND EXTRACTION
    2. 6.2 FEATURE SELECTION
    3. 6.3 LINEAR FEATURE EXTRACTION
    4. 6.4 REFERENCES
    5. 6.5 EXERCISES
  13. Chapter 7: Unsupervised Learning
    1. 7.1 FEATURE REDUCTION
    2. 7.2 CLUSTERING
    3. 7.3 REFERENCES
    4. 7.4 EXERCISES
  14. Chapter 8: State Estimation in Practice
    1. 8.1 SYSTEM IDENTIFICATION
    2. 8.2 OBSERVABILITY, CONTROLLABILITY AND STABILITY
    3. 8.3 COMPUTATIONAL ISSUES
    4. 8.4 CONSISTENCY CHECKS
    5. 8.5 EXTENSIONS OF THE KALMAN FILTER
    6. 8.6 REFERENCES
    7. 8.7 EXERCISES
  15. Chapter 9: Worked Out Examples
    1. 9.1 BOSTON HOUSING CLASSIFICATION PROBLEM
    2. 9.2 TIME-OF-FLIGHT ESTIMATION OF AN ACOUSTIC TONE BURST
    3. 9.3 ONLINE LEVEL ESTIMATION IN AN HYDRAULIC SYSTEM
    4. 9.4 REFERENCES
  16. Appendix A: Topics Selected from Functional Analysis
    1. A.1 LINEAR SPACES
    2. A.2 METRIC SPACES
    3. A.3 ORTHONORMAL SYSTEMS AND FOURIER SERIES
    4. A.4 LINEAR OPERATORS
    5. A.5 REFERENCES
  17. Appendix B: Topics Selected from Linear Algebra and Matrix Theory
    1. B.1 VECTORS AND MATRICES
    2. B.2 CONVOLUTION
    3. B.3 TRACE AND DETERMINANT
    4. B.4 DIFFERENTIATION OF VECTOR AND MATRIX FUNCTIONS
    5. B.5 DIAGONALIZATION OF SELF-ADJOINT MATRICES
    6. B.6 SINGULAR VALUE DECOMPOSITION (SVD)
    7. B.7 REFERENCES
  18. Appendix C: Probability Theory
    1. C.1 PROBABILITY THEORY AND RANDOM VARIABLES
    2. C.2 BIVARIATE RANDOM VARIABLES
    3. C.3 RANDOM VECTORS
    4. C.4 REFERENCE
  19. Appendix D: Discrete-time Dynamic Systems
    1. D.1 DISCRETE-TIME DYNAMIC SYSTEMS
    2. D.2 LINEAR SYSTEMS
    3. D.3 LINEAR TIME INVARIANT SYSTEMS
    4. D.4 REFERENCES
  20. Appendix E: Introduction to PRTools
    1. E.1 MOTIVATION
    2. E.2 ESSENTIAL CONCEPTS IN PRTOOLS
    3. E.3 IMPLEMENTATION
    4. E.4 SOME DETAILS
    5. E.5 HOW TO WRITE YOUR OWN MAPPING
  21. Appendix F: MATLAB Toolboxes Used
  22. Index