Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

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.


[b]Features:[/b]

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.

Spis treści:
[b]Preface[/b].
[b]Foreword[/b].
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.

[b]Nota biograficzna:[/b]F. van der Heijden, Laboratory for Measurement and Instrumentation, Faculty of Electrical Engineering, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
H is main research area is in Measurement and Instrumentation at an academic level and covers sensors and micro measurement systems, and image based measurement systems. He has written books, many book chapters and journal articles.
R.P.W. Duin, Pattern Recognition Group, Delft University of Technology, Department of Imaging Sciences & Technology Lorentzeg 1–2628 CD Delft, The Netherlands
His research includes: theory and applications of image processing and pattern recognition, with a special interest in sensors, measurements and measurement accuracy. He also studies the development and use of tools in this area. He has written 6 books (in English and Dutch) and many journal and conference papers and chapters in books. He is a member of Advanced School for Computing and Imaging (ASCI), Dutch Pattern Recognition Society (NVPHBV), International Association for Pattern Recognition (IAPR) and IAPR Technical Committee TC1 (statistical pattern recognition).
D. de Ridder, Pattern Recognition Group, Delft University of Technology, Department of Imaging Sciences & Technology Lorentzeg 1–2628 CD Delft, The Netherlands
He mainly works on methods for mapping high–dimensional measurement sets to low–dimensional representations, suitable for subsequent regression or classification. He works closely with R Duin and has co–written many papers with him.
D.M.J. Tax, Fraunhofer FIRST.IDA, Kekul´estr. 7, D–12489, Berlin, Germany
His main research interest is one–class classification. He was the author/co–author of many journal publications

[b]Okładka tylna:[/b]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 experiment ation platform for skilled engineers to implement and evaluate design concepts.
[b]Features[/b]:
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.

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