Contents
Abstract
ACKNOWLEDGEMENTS
Introduction
Chapter 1
 PAGE 1
PAGE 2
PAGE 3
PAGE 4
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| Fuzziness, Fuzzy Set Theory and its Applications |
ABSTRACT
FUZZINESS, FUZZY SET THEORY AND ITS APPLICATIONS
By
Wa'el S. Al-Mout
This paper introduces an information theory-based study of the uncertainty. There is uncertainty all around us in our daily lives, and also in business and industry. One cause for such uncertainties was implied earlier, i.e. a lack of sufficient accurate instruments. There may be also uncertainty resulting from the aggregation of data. How do we cope with all these uncertain data and information resources? Many approaches have been suggested and tried; these include Rescher's plausibility theory (1976); Doyle's reasoned assumption approach (1982); Rollinger's evidence space (1983); and Bundy's incidence calculus (1984). I will not discuss these many approaches but instead the most commonly used approach that is fuzzy set. Although there are some other techniques such as certainty factors, evidence theory, and probability theory, here I will concentrate on the fuzzy set theory as the best alternative that deal with uncertainty. Fuzzy sets deal with the type of uncertainty which arises when the boundaries of a class of objects are not sharply defined. Other types of uncertainties are ambiguity and probability.
As an alternative way of dealing with inaccuracy and uncertainty of data is opened by their representation as a fuzzy set. |
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