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, aThe 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 |

You must register with us as either a Registered User before you can Download this Book. You'll be greeted by a simple sign-up page.

Once you have finished the sign-up process, you will be redirected to your download Book page.

`1.`Register a free 1 month Trial Account.`2.`Download as many books as you like (Personal use)`3.`Cancel the membership at any time if not satisfied.