| Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems Contributor(s): Bardossy, Andras -. (Author), Duckstein, Lucien (Author) |
|
 |
ISBN: 0849378338 ISBN-13: 9780849378331 Publisher: CRC Press OUR PRICE: $665.00 Product Type: Hardcover Published: April 1995 Annotation: This book presents the modeling of uncertainty, vagueness, or imprecision, alias "fuzziness", in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback. -- Foreword by Didier Dubois -- Contains numerical examples and case studies from various fields -- Provides mathematical foundations and rationale, including an appendix with proofs -- Includes guidelines for model choice -- Explains both known and original techniques for constructing rules |
| Additional Information |
| BISAC Categories: - Mathematics - Science | Environmental Science (see Also Chemistry - Environmental) - Technology & Engineering | Environmental - General |
| Dewey: 511.8 |
| LCCN: 95008177 |
| Series: Systems Engineering Series |
| Physical Information: 256 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This book presents in a systematic and comprehensive manner the modeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback. The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions. Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution. |