Modeling Uncertainty with Fuzzy Logic - With Recent Theory and Applications (Hardcover, 2009 ed.)

The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, "The only satisfactory description of uncertainty is probability.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Fuzziness and Soft Computing, 240
Release date:	April 2009
First published:	2009
Authors:	Asli Celikyilmaz • I. Burhan Turksen
Dimensions:	235 x 155 x 25mm (L x W x T)
Format:	Hardcover
Pages:	400
Edition:	2009 ed.
ISBN-13:	978-3-540-89923-5
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Professional & Technical > Technology: general issues > Technical design > Computer aided design (CAD) Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-540-89923-5
Barcode:	9783540899235

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