Modeling Uncertainty with Fuzzy Logic With Recent Theory and Applications
Synopsis
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.Book details
- Edition:
- 2009
- Series:
- Studies in Fuzziness and Soft Computing (Book 240)
- Author:
- Asli Celikyilmaz, I. Burhan Türksen
- ISBN:
- 9783540899242
- Related ISBNs:
- 9783540899235
- Publisher:
- Springer Berlin Heidelberg
- Pages:
- N/A
- Reading age:
- Not specified
- Includes images:
- No
- Date of addition:
- 2022-08-05
- Usage restrictions:
- Copyright
- Copyright date:
- 2009
- Copyright by:
- N/A
- Adult content:
- No
- Language:
- English
- Categories:
- Computers and Internet, Nonfiction, Technology