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

Description:

Classification, Parameter Estimation and State Estimation is a practical and concise inter-disciplinary guide for data analysts and designers interested in advanced measurement-based systems. Highlighting the practical deployment of theoretical issues, the book provides a useful experimentation platform for skilled engineers to implement and evaluate design concepts.

Features:

• A fully integrated and unified approach to parameter estimation, pattern classification and optimal (state) estimation.
• An introduction to emerging techniques such as support vector machines and particle filtering.
• Implementations in Matlab using the PRTools toolbox, with appendices providing the necessary documentation and useful functions for this and other existing toolboxes.
• End-of-chapter exercises and numerous worked out examples within the text and on the Internet.

A valuable text for students and researchers in engineering, computer science, physics and applied mathematics, this book will also prove an essential reference for the practising civil, control, electrical and mechanical engineer.

 

Table of Contents:

Preface.

Foreword.

1. Introduction.

2. Detection and Classification.

3. Parameter Estimation.

4. State Estimation.

5. Supervised Learning.

6. Feature Extraction and Selection.

7. Unsupervised Learning.

8. State Estimation in Practice.

9. Worked Out Examples.

Appendix A: Topics Selected from Functional Analysis.

Appendix B: Topics Selected from Linear Algebra and Matrix Theory.

Appendix C: Probability Theory.

Appendix D: Discrete-time Dynamic Systems.

Appendix E: Introduction to PRTools.

Appendix F: Used MATLAB Toolboxes.

Index.