Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis / Edition 1

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Overview

Although data engineering is a multi-disciplinary field with applications in control, decision theory, and the emerging hot area of bioinformatics, there are no books on the market that make the subject accessible to non-experts. This book fills the gap in the field, offering a clear, user-friendly introduction to the main theoretical and practical tools for analyzing complex systems. An ftp site features the corresponding MATLAB and Mathematical tools and simulations.
Market: Researchers in data management, electrical engineering, computer science, and life sciences.

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Editorial Reviews

From the Publisher
"To cope with real world uncertainties and provide a philosophical and practical guide...several methodologies are presented..." (SciTech Book News, Vol. 25, No. 4, December 2001)

"...certainly a book that should be in the library of any institution where research and advanced study in fuzzy systems are carried out." (Choice, Vol. 39, No. 7, March 2002)

"...well organized, easy to read, and self-contained.... I would recommend it to anyone interested in self-study of the basic ideas of fuzzy systems..." (International Journal of General Systems, Vol. 31, No. 6, 2002)

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Product Details

  • ISBN-13: 9780471416562
  • Publisher: Wiley
  • Publication date: 7/11/2001
  • Edition number: 1
  • Pages: 296
  • Product dimensions: 6.38 (w) x 9.65 (h) x 1.00 (d)

Meet the Author

OLAF WOLKENHAUER, PhD, holds degrees from the University of Applied Sciences in Hamburg, Germany and the University of Portsmouth in England and received his doctorate in Control Engineering from the University of Manchester Institute of Science and Technology (UMIST). He currently holds joint lectureships at UMIST in the Department of Biomolecular Sciences and the Department of Electrical Engineering and Electronics (Control Systems Center).

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Table of Contents

Preface.

Acknowledgments.

Introduction.

System Analysis.

Uncertainty Techniques.

Learning from Data: System Identification.

Propositions as Subsets of the Data Space.

Fuzzy Systems and Identification.

Random-Set Modelling and Identification.

Certain Uncertainty.

Fuzzy Inference Engines.

Fuzzy Classification.

Fuzzy Control.

Fuzzy Mathematics.

Summary.

Appendices.

Index.

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