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         Fuzzy Logic:     more books (100)
  1. Fuzzy Logic with Engineering Applications, Third Edition by Timothy Ross, 2010-03-08
  2. Fuzzy Logic: The Revolutionary Computer Technology That Is Changing Our World by Daniel Mcneill, 1994-04-14
  3. Fuzzy Logic: A Practical Approach by F. Martin McNeill, Ellen Thro, 1994-08
  4. An Introduction to Fuzzy Logic for Practical Applications (Volume 0) by Kazuo Tanaka, 1996-11-15
  5. A First Course in Fuzzy Logic, Third Edition by Hung T. Nguyen, Elbert A. Walker, 2005-10-06
  6. Fuzzy Logic and NeuroFuzzy Applications in Business and Finance by Constantin von Altrock, 1996-11-18
  7. Fuzzy Logic for Business, Finance, and Management (Advances in Fuzzy Systems U Applications and Theory) (Advances in Fuzzy Systems - Applications and Theory) by George Bojadziev; Maria Bojadziev, 2007-04-24
  8. Fuzzy Logic and Control: Software and Hardware Applications (v. 2)
  9. Applied Fuzzy Arithmetic: An Introduction with Engineering Applications by Michael Hanss, 2010-11-30
  10. Fuzzy Logic: A Framework for the New Millennium (Studies in Fuzziness and Soft Computing)
  11. Neural and Fuzzy Logic Control of Drives and Power Systems by Marcian Cirstea, Andrei Dinu, et all 2002-10-08
  12. Fuzzy Logic Get Fuzzy 2 by Darby Conley, 2002-04-01
  13. An Introduction to Many-Valued and Fuzzy Logic: Semantics, Algebras, and Derivation Systems by Merrie Bergmann, 2008-01-14
  14. Fuzzy Thinking: The New Science of Fuzzy Logic by Bart Kosko, 1994-10-10

1. Fuzzy Logic - Wikipedia, The Free Encyclopedia
Retrieved from http//en.wikipedia.org/wiki/fuzzy_logic . Categories Artificial intelligence Logic in computer science Fuzzy logic
http://en.wikipedia.org/wiki/Fuzzy_logic
Fuzzy logic
From Wikipedia, the free encyclopedia
Jump to: navigation search This article or section includes a list of references or external links , but its sources remain unclear because it lacks in-text citations
You can improve this article by introducing more precise citations. For the Super Furry Animals album, see Fuzzy Logic (album) Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic . It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997). Degrees of truth are often confused with probabilities . However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

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3. CiteULike: Tag Fuzzy_logic [24 Articles]
posted to fuzzy_logic game_theory math by smmurphy on 200606-02 180620 as. Fuzzy Sets and Systems Theory and Applications (Mathematics in Science and
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Tag fuzzy_logic [24 articles]
Recent papers classified by the tag fuzzy_logic.
  • Fuzzy Cooperative Games : Cooperation with Vague Expectations (Studies in Fuzziness and Soft Computing) (27 June 2001) by Milan Mares posted to math by smmurphy on 2006-06-02 18:06:20 as Fuzzy Sets and Systems : Theory and Applications (Mathematics in Science and Engineering) (28 October 1980) by Didier J Dubois , H Prade posted to math by smmurphy on 2006-06-02 18:29:19 as Possibility Theory (29 February 1988) by Didier Dubois , Henri Prade posted to math by smmurphy on 2006-06-02 18:28:49 as Fuzzy Measure Theory (31 January 1993) by Zhenyuan Wang , George J Klir posted to math by smmurphy on 2006-06-02 23:17:27 as Fuzzy and Multiobjective Games for Conflict Resolution (Studies in Fuzziness and Soft Computing) (26 March 2001) by Ichiro Nishizaki , Masatoshi Sakawa posted to math by smmurphy on 2006-06-02 18:04:18 as Metamathematics of Fuzzy Logic (Trends in Logic) (30 November 2001) by Petr H¡jek posted to math by smmurphy on 2006-06-02 18:02:49 as Fuzzy Sets and Fuzzy Logic: Theory and Applications (11 May 1995) by George J Klir , Bo Yuan posted to math by smmurphy on 2006-06-02 18:14:49 as along with 1 person waghsk Economic Theory of Fuzzy Equilibria: An Axiomatic Analysis (Economic Theory of Fuzzy Equilibria) by Antoine Billot posted to economics math by smmurphy on 2006-06-02 18:14:41 as Algebraic Foundations of Many-Valued Reasoning (Trends in Logic) (30 November 1999)
  • 4. Netweaver:concepts:fuzzy_logic [NetWeaver Help]
    netweaver/concepts/fuzzy_logic.txt · Last modified 2007/02/13 1041 (external edit). Recent changes RSS feed Creative Commons License Donate Powered
    http://help.netweaver.rules-of-thumb.com/netweaver:concepts:fuzzy logic
    netweaver:concepts:fuzzy_logic NetWeaver Help Although the term, FuzzyLogic, has a distinctly esoteric ring to it, the concept is actually fairly simple. FuzzyLogic provides a metric for expressing the degree to which an observation on some variable belongs to a set that represents a concept. Alternatively, one might say that FuzzyLogic is concerned with “aboutness.” To make the concept clear, let’s consider a simple example.
    raw and obsolete
    !!Fuzzy node overview NetWeaver uses OR, AND, NOT, and XOR and SOR logic nodes to define the logical dependency of a network on antecedent networks and data links. If no data links antecedent to a network use fuzzy arguments, then the operation of these nodes conforms quite closely to their usage in conventional logic. The only real difference in this context between these nodes, as used in NetWeaver, and standard logic is that true = 1, false = -1 in NetWeaver. NetWeaver allows simple or calculated data links to take fuzzy arguments to determine a data value’s membership in a fuzzy set. In order for fuzzy set membership to be propagated through a knowledge base, the definitions of the conventional logical operators OR, AND, NOT, and XOR have been extended to handle measures of fuzzy set membership. The SOR node object is a unique to NetWeaver. !!Fuzzy OR node An OR node evaluates to the maximum truth value associated with a set of antecedents connected to the OR node. For example, if A, B, and C are immediate antecedents of an OR node, and they have truth values of 0.8, 0, and -1.0, respectively, then OR evaluates to 0.8.

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    16. PC AI - Fuzzy Logic
    Where Intelligent Technology Meets the Real World; Home Contents Search News Services Contact PC AI. Fuzzy Logic
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    Fuzzy Logic
    Overview : Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the uncertainty in data. It was introduced by Dr. Lotfi Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty of natural language. Fuzzy logic is useful to processes like manufacturing because of its ability to handle situations that the traditional true/false logic can't adequately deal with. It lets a process specialist describe, in everyday language, how to control actions or make decisions without having to describe the complex behavior. See "Fuzzy Logic and Neural Networks - Practical Tools for Process Management" (PC AI May/June 1994, p. 17) for a clear and concise explanation of Fuzzy Logic. Glossary Link Fuzzy Logic SUBMIT YOUR SITE
    To Expert Systems To General AI Sites Fuzzy Logic Information on the Internet
    Applications for Fuzzy Logic An interesting list of applications in which fuzzy logic has played a role.

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