Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic

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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, a€œThe only satisfactory description of uncertainty is probability.The genetic algorithm methods encode the learning parameters as genetic codes , i.e., chromosomes. ... identification methods, which implement evolutionary methods, are optimized with GA using GAOT MATLAB program [Houck et al., 1995].


Title:Modeling Uncertainty with Fuzzy Logic
Author: Asli Celikyilmaz, I. Burhan Türksen
Publisher:Springer - 2009-04-01
ISBN-13:

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