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Books > Science & Mathematics > Mathematics > Applied mathematics > Fuzzy set theory
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Introduction to Neuro-Fuzzy Systems (Paperback, 2000 ed.)
Loot Price: R1,664
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Introduction to Neuro-Fuzzy Systems (Paperback, 2000 ed.)
Series: Advances in Intelligent and Soft Computing, 2
Expected to ship within 10 - 15 working days
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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 fur specific
applications.
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