COMPLEXITY MANAGEMENT IN FUZZY SYSTEMS: A RULE BASE COMPRESSION APPROACH, VOLUME 211

GEGOV ALEXANDER

ISBN 10: 3540388834 / ISBN 13: 9783540388838

Verlag: SPRINGER, 2007

Beschreibung

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Bestandsnummer des Verkäufers AD 9783540388838

Bibliografische Details

• Titel: COMPLEXITY MANAGEMENT IN FUZZY SYSTEMS: A RULE BASE COMPRESSION APPROACH, VOLUME 211
• Verlag: SPRINGER
• Erscheinungsjahr: 2007
• Sprache: Englisch
• ISBN-10: 3540388834
• ISBN-13: 9783540388838
• Einband: Hardcover
• Auflage: Internationale Ausgabe
• Zustand: New

Über diesen Titel

Inhaltsangabe

Doing research is a great adventure As any adventure sometimes it is hard You may feel alone and with no idea where to go But if you have courage and press onwards You will eventually stand where no one has stood And see the world as no one has seen it There can be no better feeling than this! Adaptation from ‘Introduction to Research’, Tom Addis (2004) The idea about this book has been on the author’s mind for almost a decade but it was only about a couple of years ago when the underlying research process was actually started. The reason for this delay has been the insufficient spare time for research being a lecturer in a ‘new’ UK university where the emphasis is mainly on teaching. And maybe this book would have never been written if the author had not been presented with the chance of developing new teaching modules in fuzzy logic that have given him food for thought in a research related context and have helped him combine efficiently his teaching and research activities. The title of this book may sound too specialised but it has a much wider meaning. Fuzzy systems are any systems for modelling, simulation, control, prediction, diagnosis, decision making, pattern recognition, image processing, etc. which use fuzzy logic. Although fuzzy logic is an advanced extension of binary logic, the latter is still used predominantly today.

Von der hinteren Coverseite

This book presents a systematic study on the inherent complexity in fuzzy systems, resulting from the large number and the poor transparency of the fuzzy rules. The study uses a novel approach for complexity management, aimed at compressing the fuzzy rule base by removing the redundancy while preserving the solution. The compression is based on formal methods for presentation, manipulation, transformation and simplification of fuzzy rule bases, which are illustrated by algorithms as well as results from numerous examples and two case studies. The results are directly applicable or easily extendable to a wide class of fuzzy systems and detailed benchmarks for expanding these systems to new areas such as fuzzy networks and fuzzy multi-agent systems are introduced. The intended readers are people from both academia and industry, who would be interested in building and implementing advanced fuzzy systems.