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.

Review

“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)

From the Back Cover

A survey of the philosophical implications and practical applications of fuzzy systems

Fuzzy mathematical concepts such as fuzzy sets, fuzzy logic, and similarity relations represent one of the most exciting currents in modern engineering and have great potential in applications ranging from control theory to bioinformatics. Data Engineering guides the reader through a number of concepts interconnected by fuzzy mathematics and discusses these concepts from a systems engineering perspective to showcase the continuing vitality, attractiveness, and applicability of fuzzy mathematics.

The author discusses the fundamental aspects of data analysis, systems modeling, and uncertainty calculi. He avoids a narrow discussion of specialized methodologies and takes a holistic view of the nature and application of fuzzy systems, considering principles, paradigms, and methodologies along the way. This broad coverage includes:
* Fundamentals of modeling, identification, and clustering
* System analysis
* Uncertainty techniques
* Random-set modeling and identification
* Fuzzy inference engines
* Fuzzy classification, control, and mathematics

In the important emerging field of bioinformatics, the book sets out how to encode a natural system in mathematical models, describes methods to identify interrelationships and interactions from data, and thereby helps the practitioner to decide which variables to measure and why.

Data Engineering serves as an up-to-date and informative survey of the theoretical and practical tools for analyzing complex systems. It offers a unique treatment of complex issues that is accessible to students and researchers from a variety of backgrounds.

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