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1. Fuzzy Logic - Wikipedia, The Free Encyclopedia
Fuzzy logic is derived from Fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic.
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Fuzzy logic
From Wikipedia, the free encyclopedia
Jump to: navigation search 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 conceptually distinct; 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'.

2. Fuzzy Logic Tutorial - An Introduction
Fuzzy logic Tutorial. PART I Introduction to Fuzzy logic INTRODUCTION WHERE DID Fuzzy logic COME FROM? WHAT IS Fuzzy logic?
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3. Fuzzy Logic Archive
The Net s Original Fuzzy logic Archive Since 1994.
The Net's Original Fuzzy Logic Archive - Since 1994 Basics Beverly's Fuzzy Logic Bookstore Brief Course in Fuzzy Logic
Frequently Asked Questions UseNet
Eric Horstkotte on Fuzzy Logic
Part 1 - Overview Part 2 - Fuzzy Expert Systems Part 3 - Fuzzy Environmental Control
Fuzzy for Beginners ... Fuzzy Logic in Consumer Products Take the Fuzzy Shower Challenge Fuzzy Systems - A Tutorial
Fuzzy Logic Development Environment
Lotfi Zadeh - Founder of Fuzzy Logic info at SiteTerrific Web Solutions

4. FAQ: Fuzzy Logic And Fuzzy Expert Systems 1/1 [Monthly Posting]
7 What are Fuzzy numbers and Fuzzy arithmetic? 8 Isn t Fuzzy logic an inherent contradiction? Why would anyone want to fuzzify logic?

5. Fuzzy Logic
The principal constituents of soft computing (SC) are Fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter
Fuzzy Sets and Systems Lotfi A. Zadeh , The founder of fuzzy logic
New fuzzy archive by thread.
Fuzzy Logic Tools and Companies. General sources of fuzzy information.
Maintained by Bob John.
World Federation on Soft Computing
Artificial Intelligence-related Frequently Asked Questions
Professional Organizations and Networks
International Fuzzy Systems Association (IFSA)
IFSA is a worldwide organization dedicated to the support and development of the theory of fuzzy sets and systems and related areas and their applications, publishes the International Journal of Fuzzy Sets and Systems, holds International conferences, establishes chapters and sponsors other activities.
Berkeley Initiative in Soft Computing (BISC)
BISC Program is the world-leading center for basic and applied research in soft computing. The principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming belief networks, evolutionary computing including DNA computing, chaos theory and parts of learning theory.
North American Fuzzy Information Processing Society (NAFIPS)
As the premier fuzzy society in North America established in 1981, our purpose is to help guide and encourage the development of fuzzy sets and related technologies for the benefit of mankind.

6. The MathWorks - Fuzzy Logic Toolbox - Design And Simulate Fuzzy Logic Systems
The Fuzzy logic Toolbox extends the MATLAB technical computing environment with tools for the design of systems based on Fuzzy logic.
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Design and simulate fuzzy logic systems
The Fuzzy Logic Toolbox extends the MATLAB technical computing environment with tools for designing systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy clustering and adaptive neurofuzzy learning. View data sheet Contact sales Free technical kit Trial software ... RSS

7. Fuzzy Logic (Stanford Encyclopedia Of Philosophy)
The term Fuzzy logic emerged in the development of the theory of Fuzzy sets by Lotfi Zadeh (1965). A Fuzzy subset A of a (crisp) set X is characterized by
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Fuzzy Logic
First published Tue Sep 3, 2002; substantive revision Sun Jul 23, 2006 The term "fuzzy logic" emerged in the development of the theory of fuzzy sets by Lotfi Zadeh ( ). A fuzzy subset A of a (crisp) set X is characterized by assigning to each element x of X the degree of membership of x in A (e.g., X is a group of people, A the fuzzy set of old people in X ). Now if X is a set of propositions then its elements may be assigned their degree of truth intermediate connectives truth functions different from probability theory since the latter is not truth-functional (the probability of conjunction of two propositions is not determined by the probabilities of those propositions). Two main directions in fuzzy logic have to be distinguished (cf. Zadeh 1994 Fuzzy logic in the broad sense (older, better known, heavily applied but not asking deep logical questions) serves mainly as apparatus for fuzzy control, analysis of vagueness in natural language and several other application domains. It is one of the techniques of soft-computing , i.e. computational methods tolerant to suboptimality and impreciseness (vagueness) and giving quick, simple and

8. Fuzzy Logic
Fuzzy logic is basically a multivalued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false,
Fuzzy Logic
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"Fuzzy Logic is basically a multivalued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, black/white, etc. Notions like rather warm or pretty cold can be formulated mathematically and processed by computers." Bauer et al.

9. Fuzzy Logic Tutorial.
Fuzzy logic list of books (through Amazon)
FUZZY LOGIC TUTORIAL Fuzzy Logic for "Just Plain Folks" (Online Book, Free for your personal use.)
By Thomas E. Sowell, Published on the Web 2003, revised 2006. Ch. 1 of 3. Fuzzy Logic - A Powerful Way to Analyze and Control Complex Systems
Ch. 2 of 3. An Exciting Moment in the History of Science

Ch. 3 of 3. Let's Build a Fuzzy Logic Controller
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10. Fuzzy Logic Knits
logic is a Beautifully Fuzzy Thing. The Home of Mathematical Knitting, with Dr. SarahMarie Belcastro. Whom I would like to be when I grow up.
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11. What Is Fuzzy Logic ?
Fuzzy logic is a powerful problemsolving methodology with a myriad of applications in embedded control and information processing.
What Is Fuzzy Logic ? Fuzzy logic is a powerful problem-solving methodology with a myriad of applications in embedded control and information processing. Fuzzy provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. Unlike classical logic which requires a deep understanding of a system, exact equations, and precise numeric values, Fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy Logic allows expressing this knowledge with subjective concepts such as very hot, bright red, and a long time which are mapped into exact numeric ranges. Fuzzy Logic has been gaining increasing acceptance during the past few years. There are over two thousand commercially available products using Fuzzy Logic, ranging from washing machines to high speed trains. Nearly every application can potentially realize some of the benefits of Fuzzy Logic, such as performance, simplicity, lower cost, and productivity. Fuzzy Logic has been found to be very suitable for embedded control applications. Several manufacturers in the automotive industry are using fuzzy technology to improve quality and reduce development time. In aerospace, fuzzy enables very complex real time problems to be tackled using a simple approach. In consumer electronics, fuzzy improves time to market and helps reduce costs. In manufacturing, fuzzy is proven to be invaluable in increasing equipment efficiency and diagnosing malfunctions.

12. Fuzzy Logic And Its Uses
Fuzzy Rules Fuzzy Control Case Study A Smart Traffic Light Controller Adaptive Fuzzy Systems The Future References
by Shahariz Abdul Aziz and Jeyakody Parthiban Introduction Background Case Study: A Smart Traffic Light Controller ... References

13. Fuzzy Logic: Product Information
Fuzzy logic is Mathematica application software for creating Fuzzy sets and Fuzzy logic based systems. Graphics routines enable users to visualize Fuzzy
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The Most Flexible Environment for Exploring Fuzzy Systems
Fuzzy Logic brings you an essential set of tools for creating, modifying, and visualizing fuzzy sets and fuzzy logic-based systems. Ideal for engineers, researchers, and educators, Fuzzy Logic provides practical examples that introduce you to basic concepts of fuzzy logic and demonstrate how to effectively apply the tools in the package to a wide variety of fuzzy system design tasks. Experienced fuzzy logic designers will find it easy to use the package to research, model, test, and visualize highly complex systems. The package's built-in functions help you at every stage of the fuzzy logic design process as you define inputs and outputs, create fuzzy set membership functions, manipulate and combine fuzzy sets and relations, apply inferencing functions to system models, and incorporate defuzzification routines. Ready-to-use graphics routines make it easy to visualize defuzzification strategies, fuzzy sets, and fuzzy relations. "This is the most useful computational package for the use and study of fuzzy logic by practicing professionals and students."

14. BBspot - Fuzzy Logic Archives
A Very Special Fuzzy logic Fuzzy Arcade Mollytov Cocktail The Pin Slipped Caution Low Flying Geese Assault on Fort Dortmeyer
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15. Open Site - Science: Mathematics: Logic: Fuzzy Logic
Fuzzy logic is a superset of Boolean, or classical, logic dealing with the concept of partial truth. While in Boolean logic everything can be expressed in
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... Logic : Fuzzy Logic
Fuzzy Logic is a superset of Boolean, or classical, logic dealing with the concept of partial truth. While in Boolean logic everything can be expressed in binary terms (0 or 1, on or off, yes or no), fuzzy logic replaces classical truth values with degrees of truth that range between and 1.
Historical notes
The subject was introduced by Lotfi Zadeh in the 1960s, and has been rather controversial since then. It is widely accepted within the engineering and computer science communities but generally rejected by mathematicians, a majority of which does not consider it a legitimate field of logic.
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16. 'Fuzzy' Logic
Article Researchers explore agricultural applications of new computer software technology.
- Winter 1995 "Update" Newsletter Article - 'Fuzzy' logic
Researchers explore agricultural applications of new computer software technology From CATI Publication #950101
A CSU, Fresno Industrial Technology professor is exploring agricultural applications of a new technology that mimics human thought patterns in controlling industrial equipment operations.
Professor Matthew Yen's research is focused on the application of "fuzzy logic" in the control of equipment such as heaters, motors, pumps, valves and sprinklers used in the food processing industry and other automated agricultural operations.
In order to automate these processes, temperature, motor speed, liquid level, pressure, humidity, flowrate and other variables must be constantly monitored and adjusted according to prescribed schedule. Simple on/off controls may overshoot, undershoot or fluctuate around the desired setting values.
"Fuzzy logic control enables the system to tightly follow the control prescription in a smooth manner," Yen said. "It is an emerging technology widely used by the appliance industry and process industries in Japan."
The concept fuzzy logic controls was first proposed in 1965 by L. A. Zadeh and is based on the "fuzzy estimation" or "chunking" of human thinking rather than precise mathematical computation. A control system based on fuzzy logic has the following advantages: 1) It is easy to implement since it uses "if-then" logic instead of sophisticated differential equations; 2) It is understandable by people who do not have process control backgrounds; and 3) Software and hardware tools are readily available for applying this technology.

17. PC AI - 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
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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.

18. Fuzzy Logic Recordings
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19. Fuzzy Logic Functions: An Overview | The Brunching Shuttlecocks
This function returns one of the nine neoboolean values used in Fuzzy logic true, false, maybe, sure, what, whoa, depends,
Fuzzy Logic Functions: An Overview
whatever whatever LIST This function returns one of the nine neo-boolean values used in fuzzy logic: true, false, maybe, sure, what, whoa, depends, look-let's-talk-about-this-later-when-we're-not-in-public, and elbows. The value returned is determined by standard anti-random vacillation routines. reconsider reconsider EXPR This causes the program to evaluate an expression until such time as it feels reasonably sure of its conclusion. Depending on the system and expression, this may take a fraction of a second or an entire freshman semester. while holdon while ( EXPR BLOCK holdon ( EXPR BLOCK This works like a standard while loop at first, but at some point the function realizes it's been bringing personal issues into the evaluation in an inappropriate manner and begins to evaluate the expression named by holdon instead in an attempt to appear reasonable. goaway goaway LABEL This causes the program to execute starting at LABEL , while making it clear to the program that you could care less whether it ever returned to the present execution point or not. Calling the apology function later may cause the program to return to the statement directly after the goaway , but it may also cause the program to exit entirely, depending on how much you've been taking it for granted. Use of this function has been generally deprecated since the publication of the landmark essay "'GOAWAY' Considered Thoughtless."

20. Fuzzy Logic
The binary logic of modern computers often falls short when describing the vagueness of the real world. Fuzzy logic offers more graceful alternatives.
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Fuzzy Logic
The binary logic of modern computers often falls short when describing the vagueness of the real world. Fuzzy logic offers more graceful alternatives. by Bart Kosko and Satoru Isaka BART KOSKO and SATORU ISAKA are pioneers in the development of fuzzy logic systems. Kosko holds degrees in philosophy and economics from the University of Southern California, a masters in applied mathematics from the University of California, San Diego, and a Ph.D. in electrical engineering from the University of California, Irvine. He is an assistant professor of electrical engineering at U.S.C., a governor of the International Neural Network Society and the program co-chair of the 1993 World Congress on Neural Networks. Isaka specializes in fuzzy information processing in the research and development division at Omron Advanced Systems in Santa Clara, Calif. He is interested in applying machine learning and adaptive control systems to biomedical systems and factory automation. He received his M.S. and Ph.D. degrees in systems science from U.C.S.D., in 1986 and 1989, respectively. Computers do not reason as brains do . Computers "reason" when they manipulate precise facts that have been reduced to strings of zeros and ones and statements that are either true or false. The human brain can reason with vague assertions or claims that involve uncertainties or value judgments: The air is cool," or "That speed is fast" or "She is young." Unlike computers, humans have common sense that enables them to reason in a world where things are only partially true.

21. QuickStudy: Fuzzy Logic
Fuzzy logic is an extension of classic Boolean logic designed to work with imprecise or vague data. Where classical reasoning requires yes and no values,,10801,95497,00
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Get Fuzzy If 0.70 represented a probability value, we would read it as "There is a 70% chance that Russell is short," meaning that we still believe that Russell is either short or not short, and we have a 70% chance of knowing which group he belongs to. But fuzzy terminology really translates to "Russell's degree of membership in the set of short people is 0.70," by which we mean that if we take all the (fuzzy set of) short people and line them up, Russell is positioned 70% of the way to the shortest.

22. ThinkGeek :: Fuzzy Logic
Fuzzy logic extends that ability to computers by allowing for membership in more than one group. The room you re in can be both warm and suitable.
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Zoom IF closet IS running low THEN purchase shirt Computers reason in black and white. Humans, on the other hand, reason all the time in various shades of grey, with incomplete knowledge in a potentially unknown environment with imprecise elements. Fuzzy Logic extends that ability to computers by allowing for membership in more than one group. The room you're in can be both warm and suitable. Your laundry can be both wet and almost dry. While playing a video game, you can be both teh win over one player and pwned by someone else at the same time. Such is life.

23. Ortech Engineering's Fuzzy Logic Reservoir
This part of the reservoir contains references to online papers, journals, and other publications. You should get a clear understanding of Fuzzy logic and
Ortech Engineering Inc.
Fuzzy Logic Reservoir
Make sure your air tanks are full and take a deep breath before diving into this reservoir. Also be sure to string a return line during your exploration or you might never find your way back home. Gems located at this site are marked by a treasure chest all others will take you on a cyberdive. Enjoy your trip. Jump off the diving platform in one of the following directions to begin your adventure:
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This part of the reservoir contains references to on-line papers, journals, and other publications. You should get a clear understanding of fuzzy logic and its applications from this information.

24. Fuzzy Logic
A visual communications and brand development company specializing in new media. Site contains online portfolio.

25. Fuzzy Logic
Provides substantial links related to Fuzzy logic.
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Fuzzy Logic Newsletter
To obtain samples of the Fuzzy Logic/Fuzzy Control Newsletter, send any e-mail message to . To subscribe to the Fuzzy Logic/Fuzzy Control Newsletter, send an e-mail with the Subject: "Subscribe FUZ" to Technical University of Vienna Fuzzy Logic Mailing List ( send get fuzzy-mail info NAFIPS Fuzzy Logic Mailing List ( send help For data mining list send an e-mail with an empty subject heading and: subscribe datamine-l in the body of the e-mail. Contact the Editor Adaptive Automation Home Page Technology Continuum Page Artificial Intelligence Book Store ... Top of Page

26. Fuzzy’s Logic
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27. Fuzzy Logic Inc. - Website Services
Fuzzy logic Inc. supplier of website expertise and services to businesses in Calgary, Alberta.
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28. CSISS Classics - Lotfi Zadeh: Fuzzy Logic-Incoporating Real-World Vagueness
Professor Zadeh s paper on Fuzzy sets introduced the concept of a class with unsharp boundaries and marked the beginning of a new direction by providing a
Lotfi Zadeh: Fuzzy logic-Incoporating Real-World Vagueness
By Pragya Agarwal
Back to Classics Background Fuzzy logic was first invented as a representation scheme and calculus for uncertain or vague notions. It is basically a multi-valued logic that allows more human-like interpretation and reasoning in machines by resolving intermediate categories between notations such as true/false, hot/cold etc used in Boolean logic. This was seen as an extension of the conventional Boolean Logic that was extended to handle the concept of partial truth or partial false rather than the absolute values and categories in Boolean logic. Philosophers such as Plato had posited the laws of thought and one of these thoughts was the Law of Excluded Middle . Parminedes proposed the first version of this rule around 400 B.C. and stated amidst controversy that statements could be both true and not true at the same time. The Greek Philosopher Plato laid the foundations for the fuzzy logic by proposing a third region between true and false where the two notions tumbled together. In the early 1900s, Lukasiewicz extended on to the conventional bi-valued logic of Aristotle and proposed a tri-valued logic in his paper in 1920 titled On three-valued logic The fuzzy set theory was introduced by Professor Lotfi Zadeh in 1965 and can be seen as an infinite- valued logic. Lotfi Zadeh is currently serving as a director of BISC (Berkeley Initiative in Soft Computing). Prior to 1965 Zadeh's work had been centered on system theory and decision analysis. Since then, his research interests have shifted to the theory of fuzzy sets and its applications to artificial intelligence, linguistics, logic, decision analysis, control theory, expert systems and neural networks. Currently, his research is focused on fuzzy logic, soft computing, computing with words, and the newly developed computational theory of perceptions and natural language.

29. Fuzzy Logic
The tutorial will introduce the basics of Fuzzy logic for data analysis. Fuzzy logic can be used to model and deal with imprecise information,
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Fuzzy Logic author: Michael Berthold
ACAI - 05 Description: The tutorial will introduce the basics of fuzzy logic for data analysis. Fuzzy Logic can be used to model and deal with imprecise information, such as inexact measurements or available expert knowledge in the form of verbal descriptions. We will first introduce the concepts of fuzzy sets, degrees of membership and fuzzy set operators. After discussions on fuzzy numbers and arithmetic operations using them, the focus will shift to fuzzy rules and how such systems of rules can be derived from available data. Categories
Computer Science Fuzzy Logic You might be experiencing some problems with Your Video player. Slides Tutorial: Fuzzy Logic Overview Fuzzy Logic: Motivation Characteristic Functions: Crisp Sets Characteristic Functions: Fuzzy Sets Linguistic Variables and Values Types of Membership Functions Fuzzy Membership Function: Basic Concepts Operators on Fuzzy Sets - Page 1 Operators on Fuzzy Sets - Page 2 Min / Max-Norm Product / Bounded-Sum T-norms and S-norms Fuzzy T- and S-Norms - Page 1 Fuzzy T- and S-Norms - Page 2 Fuzzy Norms: Issues… Imprecise Reasoning Joint Constraint (support distribution) Conditional Constraint (possibility distribution) Joint vs. Conditional Constraint

30. FAQ: Fuzzy Logic And Fuzzy Expert Systems 1/1 [Monthly Posting]
Archivename Fuzzy-logic/part1 Last-modified Fri Mar 14 113833 1997 by Mark Kantrowitz Version 1.27 Maintainer Mark Kantrowitz et al
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FAQ: Fuzzy Logic and Fuzzy Expert Systems 1/1 [Monthly posting]
There are reader questions on this topic!
Help others by sharing your knowledge
From: (Mark Kantrowitz) Newsgroups: ... with Send Fuzzy FAQ in the message body. The FAQ postings are also archived in the periodic posting archive on [] If you do not have anonymous ftp access, you can access the archive by mail server as well. Send an E-mail message to with "help" and "index" in the body on separate lines for more information. An automatically generated HTML version of the Fuzzy Logic FAQ is accessible by WWW as part of the AI-related FAQs Mosaic page. The URL for this resource is ... . If you deposit anything in new/, please inform The repository is maintained by Timothy Butler,

31. Fuzzy Logic And Musical Decisions
Abstract This article presents some of the core concepts of Fuzzy logic and demonstrates how they may be applied to problems common in music analysis and
Fuzzy Logic and Musical Decisions
Peter Elsea, University of California, Santa Cruz Abstract: This article presents some of the core concepts of Fuzzy Logic and demonstrates how they may be applied to problems common in music analysis and composition. Contents:
Representation of pitches as sets
In the max environment, pitches are necessarily represented as numbers, typically by the MIDI code required to produce that pitch on a synthesizer. We must begin with and return to this representation, but for the actual manipulation of pitch data other methods are desirable, methods that are reflective of the phenomena of octave and key. A common first step is to translate the midi pitch number (mpn) into two numbers, representing pitch class (pc) and octave (oct) this is done with the formulas: oct = mpn / 12 pc = mpn % 12 The eventual reconstruction of the mpn is done by mpn = 12*oct + pc In this system pc can take the values - 11, in which represents a C. Oct typically ranges from to 10. Middle C, which is called C3 in the MIDI literature, and C4 by most musicians, is octave 5 after this conversion.

32. FuzzyTECH
Complete Repository for Fuzzy logic Applications, including Application Notes, Simulation Software, and Teaching Materials.
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33. FLLL - A Brief Course In Fuzzy Logic
A brief course in Fuzzy logic and Fuzzy Control. Authors. Peter Bauer Stephan Nouak Roman Winkler. Version 1.2 Date December 4, 1996
A brief course in Fuzzy Logic and Fuzzy Control
Authors: Version: 1.2
Date: December 4, 1996 You may download this course from the FTP server
Table of Contents
  • Introduction
  • Fuzzy Sets
  • Basic definitions
  • Operations on Fuzzy Sets
  • Fuzzy Control
  • Applications of Fuzzy Logic ... FLLL homepage
  • 34. EUSFLAT - European Society For Fuzzy Logic And Technology
    9th International Conference on Fuzzy Set Theory and Applications, Liptovský Mikuláš, Slovak Republic, February 48, 2008
    EUSFLAT Newsletter Vol. 3, No. 3, December 2007
    Four New EUSFLAT Working Groups
    29th Linz Seminar on Fuzzy Set Theory, Linz, Austria, February 12-16, 2008
    EUSFLAT 2007 Retrospect
    Member of the International Fuzzy Systems Association (IFSA)

    35. Fuzzy Logic -- From Wolfram MathWorld
    In the Season 4 opening episode Trust Metric (2007) of the television crime drama NUMB3RS, math genius Charlie Eppes attempts to use Fuzzy logic in an
    Search Site Algebra
    Applied Mathematics

    Calculus and Analysis
    ... Mathematics in Television
    Fuzzy Logic An extension of two-valued logic such that statements need not be true or false , but may have a degree of truth between and 1. Such a system can be extremely useful in designing control logic for real-world systems such as elevators. In the Season 4 opening episode " Trust Metric " (2007) of the television crime drama NUMB3RS, math genius Charlie Eppes attempts to use fuzzy logic in an attempt to construct a trust metric telling how much fugitive and alleged traitor Colby Granger can be trusted. SEE ALSO: Alethic False Logic True ... [Pages Linking Here] REFERENCES: Cignoli, R. L. O.; D'Ottaviano, I. M. L.; and Mundici, D. Algebraic Foundations of Many-Valued Reasoning. Dordrecht, Netherlands: Kluwer, 2000. Gottwald, S. A Treatise on Many-Valued Logics. Baldock, Hertfordshire, England: Research Studies Press, 2001. Metamathematics of Fuzzy Logic. Dordrecht, Netherlands: Kluwer, 1998. McNeill, D. Fuzzy Logic: A Practical Approach.

    36. Free Fuzzy Logic Libarary
    The Free Fuzzy logic Library (FFLL) is an open source Fuzzy logic class library and API that is optimized for speed critical applications, such as video
    Free Fuzzy Logic Library
    Home License History Class Hierarchy ... Downloads
    What is FFLL?
    The Free Fuzzy Logic Library (FFLL) is an open source fuzzy logic class library and API that is optimized for speed critical applications, such as video games. FFLL is able to load files that adhere to the IEC 61131-7 standard FFLL was a part originally released as part of the book: AI Game Programming Wisdom . A book containing over 650 pages of AI programming gems. Louder!'s own Michael Zarozinski contributed a chapter detailing the fuzzy logic class libraries that Spark! was built upon.
    What do you want to know more about?
    What's New August 8, 2003 FFLL V2.2.1 released Spark! Viewer download available - see what your fuzzy logic models look like! Sample FCL and projects files posted Louder Than A Bomb! Software

    37. Fuzzy Logic | This Headline Is (half) False |
    In order to gain access to it please either Log in, Activate your complimentary web account if you are a print subscriber, or Subscribe now. Fuzzy logic

    38. Type-2 Fuzzy Logic
    An online information resource on type2 Fuzzy logic.
    Home Publications News Events ... Contact Site Index Members Area Username: Password: Remember me Register here Forgotten your password? Site Hosted By Introduction seeks to provide the type-2 fuzzy logic community with a focal point for research, announcements and discussions. It is aimed at those already active in the field to keep abreast of developments as well as a useful resource for those new to type-2 fuzzy logic. This is your site and we hope you provide information on papers we may have missed, news items and announcements of conferences etc, This site provides the following resources:
    • A community maintained database of type-2 publications. A type-2 fuzzy logic news page. A list of forthcoming events, conferences, etc. A list of type-2 fuzzy practitioners that have signed up to this site. An interactive forum for discussing issues surrounding type-2 fuzzy logic.
    We hope you will find this site useful and interesting. This usefulness of this site relies on contributions from the community. Please take the time to register on this site and to submit content. You can contact us here , we look forward to your comments and suggestions.

    39. Fuzzy Logic Software: Type-2 Fuzzy Logic
    Software! Freeware! Fuzzy logic software! Type2 Fuzzy logic software will let you handle rule uncertainties using Fuzzy memberships.
    Type-2 Fuzzy Logic Software
    Nilesh N. Karnik, Qilian Liang, Feilong Liu and Jerry M. Mendel
    Fuzzy Logic Software Uncertain Rule-Based
    Fuzzy Logic Systems: Introduction and
    New Directions
    Fuzzy Logic Software Uncertain Rule-Based
    Fuzzy Logic Systems: Introduction and
    New Directions
    Fuzzy Logic Software Uncertain Rule-Based
    Fuzzy Logic Systems: Introduction and New Directions
    Book W e are providing software (freeware) on-line so that you can immediately begin using type-2 fuzzy logic. It is in four sections: general type-2 fuzzy logic systems, interval type-2 fuzzy logic systems, type-1 fuzzy logic systems, and NEW type-reduction. This software is experimental in nature and is provided on an "as is" basis only. The University SPECIFICALLY DISCLAIMS ALL WARRANTIES INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. B y accessing the software, you agree to abide by the conditions of the three numbered items. Select one of the following two choices. Selecting the first choice provides you with the software. Selecting the second choice sends you to the report page.

    40. Math Forum - Ask Dr. Math
    Date 04/26/97 at 033006 From Nicholas Graham Subject Fuzzy logic My calculus teacher suggested that when I get onto the Internet, I look up Dr. Math

    Associated Topics
    Dr. Math Home Search Dr. Math
    Fuzzy Logic
    Date: 04/26/97 at 03:30:06 From: Nicholas Graham Subject: Fuzzy logic My calculus teacher suggested that when I get onto the Internet, I look up Dr. Math and ask about fuzzy logic. He said the reply would be interesting. Well, here I am asking. Thank you! Nick Graham, Grade 11 Calculus Student Date: 04/26/97 at 10:07:53 From: Doctor Sarah Subject: Re: Fuzzy logic Hi Nick - Let's get you started exploring the internet. Using Altavista to search for "fuzzy logic" (we recommend that you use a searcher for a topic like this, since there's lots on the Web about it!) here are just a few examples of what you can find: (1) Fuzzy logic basics "Fuzzy Logic is a departure from classical two-valued sets and logic, that uses "soft" linguistic (e.g. large, hot, tall) system variables and a continuous range of truth values in the interval [0,1], rather than strict binary (True or False) decisions and assignments. Formally, fuzzy logic is a structured, model-free estimator that approximates a function through linguistic input/output associations. Fuzzy rule-based systems apply these methods to solve many types of "real-world" problems, especially where a system is difficult to model, is controlled by a human operator or expert, or where ambiguity or vagueness is common. A typical fuzzy system consists of a rule base, membership functions, and an inference procedure" (2) Fuzzy logic

    41. Fuzzy Logic Sources Of Information
    My personal Fuzzy logic bibliography is available here mainly references to type 2 and interval valued Fuzzy sets but also some other relating to

    42. Fuzzy Logic - A Definition From
    Fuzzy logic is an approach to computing based on degrees of truth rather than the usual true or false (1 or 0) Boolean logic on which the modern computer is,,sid9_gci212172,00.html
    fuzzy logic
    Search our IT-specific encyclopedia for: or jump to a topic: Application Security CIO CRM Data Center Data Management Domino Exchange IBM S/390 IBM AS/400 IT Channel Mobile Computing Networking Networking Channel Open Source Oracle SAP Security Security Channel Server Virtualization Small Medium Business SQL Server Storage Storage Channel Systems Channel Visual Basic VoIP Web Services Windows IT Windows Security Windows Systems Advanced Search Browse alphabetically:
    B C D ... Programming
    fuzzy logic
    Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s. Dr. Zadeh was working on the problem of computer understanding of natural language. Natural language (like most other activities in life and indeed the universe) is not easily translated into the absolute terms of and 1. (Whether everything is ultimately describable in binary terms is a philosophical question worth pursuing, but in practice much data we might want to feed a computer is in some state in between and so, frequently, are the results of computing.) Fuzzy logic includes and 1 as extreme cases of truth (or "the state of matters" or "fact") but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not "tall" or "short" but ".38 of tallness."

    43. Fuzzy Logic Tutorial
    A 6part tutorial on Fuzzy logic covering sets, rule matrices, membership functions, A instructive paper on Fuzzy logic (by Tung Tran and Ali Zomorodi).
    Go Home

    Fuzzy Logic
    This site is no longer being maintained. Sorry. OUTLINE TUTORIAL TOOLS RESOURCES ... SUBMIT
    • A 6-part tutorial on fuzzy logic covering sets, rule matrices, membership functions, etc. Also has some sample problems. A fuzzy tutorial with an explanation of fuzzy sets. A brief course in Fuzzy Logic. A instructive paper on fuzzy logic (by Tung Tran and Ali Zomorodi). A large archive of fuzzy logic tutorials.
    Submit a tutorial on Fuzzy Logic.
    Last updated on Tuesday, April 13, 2004 04:06:50 PM.
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    The FuzzyJ Toolkit is a set of Java(tm) classes that can be used to build Fuzzy logic systems. The toolkit s API can be used standalone to create Fuzzy
    Fuzzy Logic in Integrated Reasoning FuzzyCLIPS FuzzyCLIPS is an extension of the CLIPS FuzzyJ Toolkit for the Java(tm) Platform and FuzzyJess The FuzzyJ Toolkit is a set of Java(tm) classes that can be used to build fuzzy logic systems. The toolkit's API can be used standalone to create fuzzy rules and perform fuzzy reasoning. It can also be used with Jess , the Expert System Shell from the Sandia National Laboratories providing a capability that is similar to but in many ways more flexible than FuzzyCLIPS. This integration with Jess, FuzzyJess , has been carefully planned with the Jess author so that the effort to maintain and follow Jess revisions will require minimal effort. The toolkit is currently at version 1.10a (***NEW*** as of September 5, 2006). User feedback is welcomed and will help us shape future versions of the FuzzyJ Toolkit.
    Comments and questions should be addressed to Last modified: September 5, 2006.

    45. Fuzzy-Logik - Wikipedia
    Translate this page Zuvor hatte Joseph Goguen, ein Doktorand von Zadeh, eine „logic of inexact concepts“ eingeführt. Heute wird die Fuzzy-Logik oder Fuzzy-Control vorwiegend
    var wgNotice = ""; var wgNoticeLocal = ""; var wgNoticeLang = "de"; var wgNoticeProject = "wikipedia";
    aus Wikipedia, der freien Enzyklop¤die
    Wechseln zu: Navigation Suche Fuzzy-Logik (englisch: fuzzy = unscharf ) ist eine Theorie, eine Verallgemeinerung der zweiwertigen Booleschen Logik , die vor allem f¼r die Darstellung menschlichen (und damit unscharfen) Wissens entwickelt wurde. Fuzzy-Computersysteme verarbeiten gegen¼ber herk¶mmlichen Systemen nicht nur Werte wie JA und NEIN (oder AN und AUS oder 1 und ), sondern zus¤tzlich als Zwischenwert (Wahrheitswert) zwischen WAHR (=1) und FALSCH (=0) etwa , so dass damit auch unscharfe Angaben wie EIN BISSCHEN, ZIEMLICH oder STARK mathematisch behandelt werden k¶nnen (vgl. auch Fuzziness (Sprache) ). Damit arbeiten fuzzylogikunterst¼tzte Programme n¤her am menschlichen Denken als ¼bliche Programme. Die Fuzzy-Set-Theorie , also die unscharfe Mengenlehre , wurde bereits 1965 von L. A. Zadeh , einem aus Aserbaidschan stammenden Professor f¼r Elektrotechnik an der University of California, Berkeley

    46. Fuzzy Logic - A Comic By Mike Stanfill
    Fuzzy logic by Mike Stanfill ©2002. 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 • 9 • 10 • 11 • 12 • 13 • 14 • 15 • 16 • 17 • 18

    47. Intelligent Solutions, Inc. Petroleum & Natural Gas Engineering Consultant
    Fuzzy logic is the focus of this article. An overview of the subject is provided In this article, application of Fuzzy logic for restimulation candidate
    Distinguished Author Series JPT - Journal of Petroleum Technology, November 2000 VIRTUAL INTELLIGENCE AND ITS APPLICATIONS IN PETROLEUM ENGINEERING Part 3. Fuzzy Logic Shahab Mohaghegh
    West Virginia University In two previous articles, a general overview of artificial neural networks and evolutionary computing and their applications in the oil and gas industry was presented. Fuzzy logic is the focus of this article. An overview of the subject is provided followed by its potential application in solving petroleum engineering related problems. As it was mentioned in the previous articles, the most successful applications of intelligent systems, especially when solving engineering problems, have been achieved by using different intelligent tools in concert and as a hybrid system. In this article, application of fuzzy logic for restimulation candidate selection in a tight gas formation in the Rocky Mountains will be reviewed. This particular application was chosen because it uses fuzzy logic in a hybrid manner integrated with neural networks and genetic algorithms. BACKGROUND The science of today is based on Aristotle’s crisp logic formed more than two thousand years ago. The Aristotelian logic looks at the world in a bivalent manner, such as black and white, yes and no, and and 1. Development of the set theory in the late 19

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    49. Fuzzy Multidimensional Logic
    Bivalent, or two state, logic is just a subset of the more powerful methods of Fuzzy logic, introduced here, which reject the law of the excluded middle.
    Fuzzy Multidimensional Logic
    by Chris Lucas
    "No assertion is ever known with certainty...
    but that does not stop us making assertions."
    Carneades, 214-129 BCE "The facts were always fuzzy or vague or inexact... Science treated the gray or fuzzy facts as if they were the black-white facts of math. Yet no one had put forth a single fact about the world that was 100% true or 100% false." Bart Kosko, Fuzzy Thinking, 1994, Preface
    Logic to most people relates to two state thinking, the idea that the outcome can only be either true or false, 1 or 0, right or wrong. This form of logic dates back to ancient Greece and is perfectly adequate to answer simple questions in single dimensions, for example, if A is 1 and B is what is A AND B ? It can be extended, as is done in Boolean algebra to more complex questions, as long as all the parts can be described using the same restricted alphabet of two symbols. Such logic is a deductive way of understanding consequences, and a highly valuable intellectual technique. But this sort of logic is inadequate when we need to reason about variables that have more than two values, or in cases where multiple incompatible variables are involved. Yet we still need to make decisions in these cases, so how can we proceed ? Bivalent, or two state, logic is just a sub-set of a more powerful type of logic known as fuzzy logic, so here we will introduce this subject and show how it can be used to evaluate choices both in the multi-valued scenarios of one variable and more importantly in the multi-dimensional scenarios common to complex systems incorporating multiple variables.

    50. Definition: Fuzzy Logic From Online Medical Dictionary
    These ordinary terms represent Fuzzy sets in a particular problem. Fuzzy logic plays a key role in many medical expert systems. (12 Dec 1998) logic

    51. Article#2 On Fuzzy Logic And Its Uses
    This article describes the procedures required in designing Fuzzy logic machines starting with the basics, Fuzzy rules, moving on to the Fuzzy controller
    Everything You've Always Wanted to know About Designing Fuzzy Logic Machines But Were Afraid to Ask
    [Fuzzy Rules]
    [Fuzzy Control] [Case Study: Fuzzy Traffic Light Controller] Abstract
    The Smart Air Conditioner : automatically adjusts the flow of air according to the surrounding temperature. The Smart TV : adjusts its contrast and colour modes for every new frame. The Smart Washing Machine : adds more detergent when there is more dirt. All with the push of a button. Great!!! We are witnessing a miracle in technology. Too good to be true. But enough!!! How do such miracles occur? How complicated is the math ? HOW DOES IT WORK!!!! This article describes the procedures required in designing fuzzy logic machines starting with the basics, fuzzy rules, moving on to the fuzzy controller and finally considering a case study where a real life useful application of fuzzy logic is applied. Fuzzy Rules
    Human beings make descisions based on rules. Even though, we may not be aware of it, all the descisions we make are based on computer like if-then statements. If the weather is fine, then we may decide to go out. If the forecast says the weather will be bad today, but fine tommorow, then we make a descision not to go today, and postpone it till tommorow. Rules associate ideas and relate one event to another.
    Fuzzy machines, which always tend to mimick the behaviour of man, work the same way. Only this time the descision and the means of choosing that descison are replaced by fuzzy sets and the rules are replaced by fuzzy rules. Fuzzy rules also operate using a series of if-then statements. For instance, X then A, if y then b, where A and B are all sets of X and Y. Fuzzy rules define fuzzy

    52. XpertRule Software Ltd | Fuzzy Logic
    Humans can reason very effectively with such Fuzzy definitions, therefore, in order to capture human Fuzzy reasoning we need Fuzzy logic.
    Home Contact Products User Stories ... About Us
    White Paper: Fuzzy Logic in Knowledge Builder
    Rule based logic has been used to capture human expertise in classification, assessment, diagnostic and planning tasks. Probability has traditionally been used to capture decision making under uncertain conditions. For example, consider the rule:
    IF Symptom-A is present THEN diagnosis is illness-X
    There will be situations in which we are uncertain about the presence of Symptom-A. In such cases we can enter the probability of Symptom-A being present which will result in a confidence factor in our diagnosis of illness-X. A number of methods have been used to propagate probabilities during rule based inference. Many of these techniques are based on the probabilistic inference techniques used in Mycin and Prospector . The weakness of such techniques is that they do not reflect the way human experts reason under uncertainty. XpertRule Knowledge Builder allows an alternative methodology to the probabilistic reasoning approach. This involves defining Symptom-A and illness-X as logical attribute with values

    53. Fuzzy Sets And Fuzzy Logic
    This section contains an overview of Fuzzy Sets and Fuzzy logic. This information is taken from Fuzzy Sets and Fuzzy logic Theory and Applications,
    Fuzzy Sets and Fuzzy Logic
    Brian T. Luke, Ph.D. (
    This section contains an overview of Fuzzy Sets and Fuzzy Logic. This information is taken from Fuzzy Sets and Fuzzy Logic: Theory and Applications , George J. Klir and Bo Yuan, Prentice Hall, NJ (1995) Given three fuzzy sets (A, B, C), they each have associated membership functions (Ma, Mb, Mc). Since there is no ambiguity, A can be interchanged with Ma, B with Mb, and C with Mc. Therefore, throughout this text, A represents both a fuzzy set and its associated membership function. If x is the parameter or value that determines which set(s) the data belongs to, the membership functions can be written as A(x), B(x), and C(x). An alpha-cut of the membership function A (denoted aA) is the set of all x such that A(x) is greater than or equal to alpha (a). Similarly, a strong alpha-cut (denoted a+A) is the set of all x such that A(x) is strictly greater than alpha (a). Mathematically, "That is, the alpha-cut (or the strong alpha-cut) of a fuzzy set A is the crips set aA (or the crisp set a+A) that contains all the elements of the universal set X whose membership grades in A are greater than or equal to (or only greater than) the specified value of alpha." aA and a+A are crisp sets because a particular value x either is or isn't in the set; there is no partial membership.

    54. World Scientific
    (5) Methods in Cardiovascular and Brain Systems; Methodologies of Using Neural Network and Fuzzy logic Technologies for Motor Incipient Fault Detection
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    56. ITWire - Fuzzy Logic - Your Personal Technology Evangelist
    Technology news, views and jobs, Digital pirates busted in Sydney and Melbourne, 2 million Next G customers get cheaper phone browsing, Ribbit the Silicon
    /*********************************************** * Ajax Tabs Content script- © Dynamic Drive DHTML code library ( * This notice MUST stay intact for legal use * Visit Dynamic Drive at for full source code ***********************************************/ Today on iTWire VPN IT News Telecommunications ... IT and TELCO JOBS
    Australian IT JOBS Sydney IT jobs UNIX jobs, Linux jobs, ... Our Blogs Fuzzy Logic - Your personal technology evangelist Fuzzy Logic - Your personal technology evangelist Here comes Dell’s Vostro ‘thin and light’ 12-incher User Rating:
    By Alex Zaharov-Reutt Wednesday, 19 December 2007
    Tags: Alex Zaharov-Reutt Hardware mobility Vista ... 2.3 trillion SMS messages to be sent in 2008 costing $60.2 billion! User Rating:
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    Tags: Alex Zaharov-Reutt cellphones email messaging ... Ribbit: the Silicon Valley ‘Voice 2.0’ answer to the telephone User Rating:
    By Alex Zaharov-Reutt Tuesday, 18 December 2007

    57. What Is Fuzzy Logic ? Are There Computers That Are Inherently
    Your confusion is understandable; the term Fuzzy logic is now as likely to appear in advertising copy as in technical journals. A number of workers wrote

    Spanish Association of Fuzzy logic and Technologies.
    Asociacion FLAT
    Institut d'Investigacio en Intel.ligencia Artificial
    Campus de la Universitat Autonoma de Barcelona,
    08193 Bellaterra
    Barcelona. Spain
    tlf: 93-580 95 70
    fax: 93-580 96 61
    Spanish version of this page Father member of the International Fuzzy Systems Association (IFSA)
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    59. Fuzzy Logic -- From Eric Weisstein's Encyclopedia Of Scientific Books
    Fuzzy logic The Discovery of a Revolutionary Computer Technology and How It is Changing Our World. New York Simon and Schuster, 1993. Out of print. $?
    Fuzzy Logic
    see also Fuzzy Logic Klir, George J. and Yuan, Bo. Fuzzy Sets and Fuzzy Logic: Theory and Applications. P-H. 574 p. $78. McNeill, Dan. Fuzzy Logic: A Practical Approach. New York: Academic Press, 1994. $?. McNeill, Dan and Freiberger, Paul. Fuzzy Logic: The Discovery of a Revolutionary Computer Technology and How It is Changing Our World. New York: Simon and Schuster, 1993. Out of print. $?. Nguyen, Hung T. and Walker, Elbert A. A First Course in Fuzzy Logic. Boca Raton, FL: CRC Press, 1996. 288 p. $69.95. Rescher, Nicholas. Many Valued Logic. Ashgate, 1993. $65.95. Yager, Ronald R. and Zadeh, Lofti A. (Eds.) An Introduction to Fuzzy Logic Applications in Intelligent Systems. Boston, MA: Kluwer Academic, 1992. 356 p. $119. Zadeh, Lofti and Kacprzyk, Janusz (Ed.). Fuzzy Logic for the Management of Uncertainty.
    Eric W. Weisstein

    60. Reason Magazine - Fuzzy Logic
    Fuzzy logic. Lynn Scarlett March 1999 Print Edition. Principles for a Free Society Reconciling Individual Liberty with the Common Good,
    @import "/media/css/tf.css"; /* layout - screen only*/ @import "/media/css/reason.css"; /* layout - screen only*/ Reason Magazine
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    Lynn Scarlett Print Edition Principles for a Free Society: Reconciling Individual Liberty with the Common Good , by Richard Epstein, Reading, Mass.: Perseus Books, 368 pages, $27.50 These days, fashionable environmentalists sound like neoclassical economists. They chatter about the need to "internalize externalities," pressing for eco-taxes on greenhouse gases, sulfur dioxide emissions, wasteful packaging, noise, fumes, mine tailings, chemical consumption, and any other perceived "bad" that captures their attention. This notion of externalities as a form of "market failure" opens up endless possibilities for state intervention. The possibilities are endless because, as Richard Epstein observes in his latest book

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    Choose a topic Technical Reference Selection Guide GENERAL - Glossary - Intrinsic Safety - PFA Fluorocarbon TEMPERATURE RELATED - ITS-90 - Thermocouple Ref Tables - Practical Temp Measurement - Intro to Thermocouples - Thermocouple Color Codes - Thermocouple Junction - Fiber Optics - Fuzzy Logic - Heat Wave - Wire Insulation Identification - Infrared Temp Measurment PRESSURE, STRAIN AND FORCE - FAQ - Pipe Thread Dimensions - Pressure Transducers - Strain Gage Technical Data - Waterhammer - Pressure Units Converter FLOW AND LEVEL RELATED - Liquid Flowmeters - Selecting A Flow Meter - Technical Principles of Valves CONDUCTIVITY/ENVIRONMENT - pH Control - Introduction to pH - Dissolved Oxygen - Conductivity and Resistivity - Electrode Basics - Halogen Leak Detector - Ion Selective Electrodes - Turbidity Measurement - Water and Wastewater DATA ACQUISITION RELATED - Data Acquisition Knowledge Base - Transactions Volume 2: Data Acquisition - Based Acquisition Systems - Data Acquisition Systems - Electronic Basics - Data Logging Systems - The RS-232 Standard ELECTRIC HEATER RELATED - Electric Heater Related Fuzzy Logic On/off, PID, microprocessor based, Fuzzy Logic; the evolutionary changes of controls. This brief paper outlines some background of standard PID control and how the implementation of Fuzzy Logic can improve your single feedback control systems. Fuzzy Logic is a particular area of concentration in the study of Artificial Intelligence and is based on the value of that information which is neither definitely true nor false. The information which humans use in their everyday lives to base intuitive decisions and apply general rules of thumb can and should be applied to those control situations which demand them. Acquired knowledge can be a powerful weapon to combat the undesired effects of the system response.

    62. Mathematical Introduction To Fuzzy Logic, Fuzzy Sets, And Fuzzy Controls - Maple
    Fuzzy logic is a multivalued logic with truth represented by a value on the closed interval 0, 1, where 0 is equated with the classical false value and 1

    63. Fuzzy Math Sets, Quick Tutorial On Fuzzy Logic And Sets.
    Fuzzy logic and Fuzzy sets helper with math tests exercises and tutorial.
    Data Mining

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    Neural Networks Fuzzy Logic and Sets Genetic Algorithms LABS Fuzzy Logic Artificial Life Fuzzy Logic: A type of logic for processing imprecise data founded by L. A. Zadeh. Elements may have infinite gradation between TRUE and FALSE. Fuzzy Sets: A type of sets in which elements belong to subsets in some degree. (Certain speed may be 0.75 SLOW and 0.25 MEDIUM). Click for more fuzzy TEST A: Enter a new speed B: Speed = C: What is the fuzzy value of MEDIUM ? D: Answer with Keyboard »» KEYBOARD D: Press Enter and View report REPORT Yours: Ours: Result: Time: Home Site Map Suggest a link Send Comments ... Help wgonz @ - All Rights Reserved

    64. Welcome To Fuzzy Logic
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    65. Fuzzy Logic
    Many popular descriptions claim that Fuzzy logic represents a significant shift in outlook, i.e., a new way of thinking about the world, and that this
    Fuzzy Logic
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    66. Fuzzy Logic: How Webkinz Is Getting Young Kids Hooked On The Web - Network World
    If you have or know a kid around 6 to 8 years old then you probably have heard of Webkinz and chances are you too may have been bitten by the bug.
    var outerref = new String("(none)"); var nwchannel = 'cn'; var rxprimarytopic = 'software'; var refresh = 0; var jq_request_uri = '/news/2007/040207-wider-net-webkinz.html'; var jq_doc_uri = '/news/2007/040207-wider-net-webkinz.html'; var jq_site = 'software'; var jq_rxid = '97521'; intextsrc = ";pos=pixel;sz=1x1;ord="; bannersrc = ";pos=top;sz=728x90;ptile=1;type=news;ord="; Click Here Monday, December 24, 2007 Click Here

    67. Using Fuzzy Logic For Molecular Modeling
    Since Fuzzy logic is, by definition, imprecise, it is a natural means of representing the imprecision of lattice parameters and bond angles.
    This article is one of four papers on modeling and simulation (part one) to be presented exclusively on the web as part of the August 1999 JOM-e JOM The second part of this topic supplements the September issue. The coverage was developed by Steven LeClair of the Materials Directorate Air Force Research Laboratory Wright-Patterson Air Force Base The following article appears as part of JOM-e JOM is a publication of

    Modeling and Simulation, Part I: Overview
    Using Fuzzy Logic for Molecular Modeling
    • FUZZY MOLECULAR MODELING Since fuzzy logic is, by definition, imprecise, it is a natural means of representing the imprecision of lattice parameters and bond angles. Fuzzy lattice parameters are created by collecting parameter values from the literature. From the minimum, maximum, and average of these parameter values, a fuzzy number is created that represents the imprecision in that parameter. Then, through the use of fuzzy arithmetic operators and recently developed fuzzy trigonometric functions, fuzzy atom locations within the unit cell and bond angles can be calculated. The benefit of using fuzzy logic in this manner is that it directly enables representation and calculation with the imprecision found in chemical compounds, which arises from measurement variability due to measurement error, structural defects, and thermal and vibrational characteristics.

    68. FLAR: Fuzzy Logic In Autonomous Robotics
    This is the entry point to a set of pages devoted to the use of Fuzzy logic for Autonomous Robot navigation (FLAR). If you would like to contribute your
    Internet Resource on Using Fuzzy Logic in Autonomous Robotics Maintained by Alessandro Saffiotti This is the entry point to a set of pages devoted to the use of Fuzzy Logic for Autonomous Robot navigation (FLAR) Currently available resources Book on fuzzy logic techniques for autonomous vehicle navigation HTML Survey Paper on the use of fuzzy logic in autonomous robot navigation HTML Bibliography on fuzzy logic approaches to autonomous robotics HTML BibTeX On-Line Tutorial on fuzzy logic in autonomous robotics HTML On-Line Paper on the use of fuzzy logic in the autonomous robot Flakey HTML PostScript On-Line Paper on the use of fuzzy logic in RoboCup HTML PostScript 1st On-Line Workshop on FLAR at WSC1 (August 1996) HTML
    Website hosted by
    Last updated on Apr 3, 2002

    69. Soft Computing And Fuzzy Logic
    Soft Computing Fuzzy logic Topics Manyvalued logics, Lukasiewicz logic, t-norm based logics, MV-algebras, Fuzzy control.
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    70. Fuzzy Logic In Environmental Sciences: A Bibliography
    We are preparing a chapter on Fuzzy logic for a handbook in AI. The handbook is being assembled by AIRIES (Artificial Intelligence Research in Environmental
    Fuzzy Logic in Environmental Sciences: A Bibliography
    Here is a large collection of references to environmental research and meteorologial applications that use fuzzy logic . Researchers are listed alphabetically:
    A B C D ... Z
    Domains of interest include:
    Agriculture , climatology, earthquakes ecology environmental sciences fisheries , geography, geology hydrology meteorology mining , natural resources, oceanography petroleum pollution , risk analysis, and rivers+lakes
    If you know of work that fits in these categories, please write to us and we will add them. If you would like to be included in the list below, send us a note about yourself and your topics. We are preparing a chapter on fuzzy logic for a handbook in AI. The handbook is being assembled by AIRIES (Artificial Intelligence Research in Environmental Sciences). The book is intended to introduce environmental scientists to practical, state-of-the-art AI techniques. Many references were kindly given to us when we posted questions on newsgroups for the above-listed domains and on the newsgroup

    71. Xfuzzy Home Page
    FLEB is an ebook that attempts to introduce the basic mathematical foundations and applications of Fuzzy logic through a software environment which
    FUZZY LOGIC DESIGN TOOLS The fuzzy system development environment Xfuzzy integrates a set of tools that ease the user to cover the several stages involved in the design process of fuzzy logic-based inference systems, from their initial description to their final implementation. The sections of this page are linked with the several versions of the environment, with our related scientific publications, and with some didactic material. This new version of Xfuzzy is based on a new specification language (XFL3) which extends the advantages of its predecessor, allowing the use of linguistic hedges as well as new fuzzy operators defined freely by the user. New CAD tools have been included to ease the edition of operator sets and hierarchical systems, to generate 2- and 3-dimmensional graphic outputs, and to monitor the inference process. The tool that applies supervised learning has been quitely renewed so as to include new algorithms as well as pre- and post-processing techniques to simplify the obtained rule bases. Xfuzzy 3.0 has been enterely programmed in Java. Hence, it can be executed on any platform with JRE (Java Runtime Environment) installed. The version 2.1 of Xfuzzy, based on the specification language XFL, includes several CAD tools to describe, verify and synthesize (into software or hardware) fuzzy systems. This version can be compiled and executed in Unix-like operating systems with the X Window system. It can be also executed in MS-Windows by using the environment

    72. Fuzzy Logic Laboratorium Linz - Hagenberg
    Translate this page Department of Knowledge-Based Mathematical Systems. Fuzzy logic Laboratorium Linz-Hagenberg Fuzzy Sets, Probability, and Statistics – Gaps and Bridges
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    73. Fuzzy Logic Control With The Intel 8XC196 Embedded Microcontroller
    Fuzzy logic control is being increasingly applied to solve control problems in areas where system complexity, development time and cost are the major issues

    74. Fuzzy Logic
    This paper gives a general overview of Fuzzy logic theory. It describes the concepts of Fuzzy sets and operations used in their manipulation, developed by
    Fuzzy Logic
    This paper gives a general overview of fuzzy logic theory. It describes the concepts of fuzzy sets and operations used in their manipulation, developed by Lofti Zadeh in 1965. The paper gives examples of the fuzzy logic applications, with emphasis on the field of artificial intelligence.
    Fuzzy Logic
    "When Theseus returned from slaying the Minotaur, says Plutarch, the Athenians preserved his ship, and as planks rotted, replaced them with new ones. When the first plank was replaced, everyone agreed it was still the same ship. Adding a second plank made no difference either. At some point, the Athenians may have replaced every plank in the ship. Was it a different ship? At what point did it become one?" [1] The classical logic relies on something being either True or False. A True element is usually assigned a value of 1, while False has a value of 0. Thus, something either completely belongs to a set or it is completely excluded from it. The fuzzy logic broadens this definition of membership. The basis of the logic are fuzzy sets. Unlike in "crisp" sets, where membership is full or none, an object is allowed to belong only partly to one set. The membership of an object to a particular set is described by a real value from the range between and 1. Thus, for instance, an element can have a membership value 0.5, which describes a 50% membership in a given set. Such logic allows a much easier application of many problems that cannot be easily implemented using classical approach.

    75. North American Fuzzy Information Processing Society Nafips
    The NAFIPS Home Page has moved. Please change your bookmarks to point to You will be redirected in 15sec use the link above if this
    The NAFIPS Home Page has moved. Please change your bookmarks to point to:
    You will be redirected in 15sec - use the link above if this doesn't happen.

    76. TQ Education And Training Ltd - Site Map
    Site Map Main. The page which you have requested is not available on the TQ.COM website. Use the links below to help you on your way.
    @import "default.css"; HOME CONTACT US SITE MAP Teaching Products ... Main
    Site Map > Main
    The page which you have requested is not available on the TQ.COM website. Use the links below to help you on your way. Home Teaching Products Product News
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