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Techniques in Fuzzy Inference Neural Networks for Fuzzy Model Improvement and Their Application by Lee, Keon-Myung is a scholarly article available to read on EtoBox.

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<p>Applications of fuzzy theory (often referred to as "fuzzy logic") are maturing and multiplying at a phenomenal rate, and a comprehensive treatment of these real-world techniques and applications is now very timely. Unlike traditional computer logic involving clear true or false decisions, a fuzzy logic system chooses what is most true after "considering" several contributing and possibly conflicting variables. Examples of practical devices using fuzzy computer decision-making are thermostats that respond to a combination of temperature and humidity (comfort factors), an elevator that considers how crowded a car is rather than just its proximity to the desired floor, and a camera that integrates the variables affecting picture quality. These volumes will present a logical progression from implementation and modeling techniques to industrial/commercial applications to fuzzy neural and adaptive fuzzy systems.</p> <p><b>Audience:</b> Students, research workers, and practitioners in engineering and computer science. </p>

Author

Lee, Keon-Myung

Publisher

Elsevier

Published

1999

Language

EN

ISBN

9780124438712