Fuzzy sets were introduced by Zadeh (1965) as a means of representing
and manipulating data that was not precise, but rather fuzzy. Fuzzy
logic pro- vides an inference morphology that enables approximate human
reasoning capabilities to be applied to knowledge-based systems. The
theory of fuzzy logic provides a mathematical strength to capture the
uncertainties associ- ated with human cognitive processes, such as
thinking and reasoning. The conventional approaches to knowledge
representation lack the means for rep- resentating the meaning of fuzzy
concepts. As a consequence, the approaches based on first order logic
and classical probablity theory do not provide an appropriate conceptual
framework for dealing with the representation of com- monsense
knowledge, since such knowledge is by its nature both lexically
imprecise and noncategorical. The developement of fuzzy logic was
motivated in large measure by the need for a conceptual framework which
can address the issue of uncertainty and lexical imprecision. Some of
the essential characteristics of fuzzy logic relate to the following
[242]. - In fuzzy logic, exact reasoning is viewed as a limiting case
of ap- proximate reasoning. - In fuzzy logic, everything is a matter of
degree. - In fuzzy logic, knowledge is interpreted a collection of
elastic or, equivalently, fuzzy constraint on a collection of
variables. - Inference is viewed as a process of propagation of elastic
con- straints. - Any logical system can be fuzzified. There are two main
characteristics of fuzzy systems that give them better performance für
specific applications.
