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         Fuzzy Logic:     more books (100)
  1. A First Course in Fuzzy Logic, Third Edition by Hung T. Nguyen, Elbert A. Walker, 2005-10-06
  2. Fuzzy Logic with Engineering Applications by Timothy J. Ross, 2004-08-16
  3. Fuzzy Thinking: The New Science of Fuzzy Logic by Bart Kosko, 1994-10-10
  4. Fuzzy Sets and Fuzzy Logic: Theory and Applications by George J. Klir, Bo Yuan, 1995-05-21
  5. Fuzzy Logic: Intelligence, Control, and Information by John Yen, Reza Langari, 1998-11-23
  6. Fuzzy Logic Get Fuzzy 2 by Darby Conley, 2002-04-01
  7. Fuzzy Logic and NeuroFuzzy Applications in Business and Finance by Constantin von Altrock, 1996-11-18
  8. An Introduction to Fuzzy Logic for Practical Applications by Kazuo Tanaka, 1996-11-15
  9. Fuzzy Logic: The Revolutionary Computer Technology That Is Changing Our World by Daniel Mcneill, 1994-04-14
  10. An Introduction to Many-Valued and Fuzzy Logic: Semantics, Algebras, and Derivation Systems by Merrie Bergmann, 2008-01-14
  11. An Introduction to Fuzzy Logic and Fuzzy Sets (Advances in Soft Computing) by James J. Buckley, Esfandiar Eslami, 2002-10-29
  12. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions by Jerry M. Mendel, 2001-01-01
  13. Fuzzy Logic for Beginners by Masao Mukaidono, 2001-02-28
  14. Fuzzy Logic for Embedded Systems Applications (Embedded Technology) by Ahmad Ibrahim, 2003-09-26

1. Fuzzy Logic
Lotfi A. Zadeh, The founder of fuzzy logic. Personal Home Pages of Fuzzy Researchers Please send me the URL of your Home Page. fuzzy logic Journals and Books.
Fuzzy Sets and Systems Lotfi A. Zadeh , The founder of fuzzy logic
Personal Home Pages of Fuzzy Researchers
Please send me the URL of your Home Page.
Who is Who in Fuzzy Database.
New fuzzy archive by thread.
Fuzzy-Mail Archives.
Old fuzzy archive by thread.
Fuzzy Logic Tools and Companies. General sources of fuzzy information.
Maintained by Bob John.
Conferences and Workshops on Fuzzy Systems: 1990-2001
From the Parallel and Distributed Processing Laboratory of the Department of Applied Informatics , University of Macedonia, Thessaloniki, Greece
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.
Japan Society for Fuzzy Theory and Systems (SOFT)
Established in 1989. SOFT has 1,670 individual members and 74 company members, publishes an official bimonthly journal and organizes fuzzy systems symposiums. There are 8 regional branches and 8 research groups in SOFT.

2. Ortech Engineering's Fuzzy Logic Reservoir
by Ortech Engineering Inc.
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:
The Diving Platform
Enjoy Our Crystal Clear Waters
On-line Papers, Journals, and other Publications
Visit Our Coral Reefs

Fuzzy Logic Labs at Universities
See the Colorful Fish in the Sea

A Virtual Who's Who of Fuzzy Logic on the Internet
Discover the Lost Continent of Atlantis

Fuzzy Logic Societies and Organizations
Explore Underwater Caverns

Fuzzy Logic Research Centers Relax and Visit With Other Divers Newsgroups and Archives Watch Out for the Sharks Commercial Product and Service Providers
Our Crystal Clear Waters
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.

3. Fuzzy Logic
Survey of logical systems with a continuum of truth values; from the Stanford Encyclopdia by Petr Hajek .
version history

Stanford Encyclopedia of Philosophy
A B C D ... Z
This document uses XHTML-1/Unicode to format the display. Older browsers and/or operating systems may not display the formatting correctly. last substantive content change
Fuzzy Logic
The term "fuzzy logic" emerged in the development of the theory of fuzzy sets by Lotfi Zadeh [ Zadeh (1965) ]. 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

4. Fuzzy Logic Archive
The Net s Original fuzzy logic Archive Since 1994, Basics. Beverly s fuzzy logic Bookstore. Brief Course in fuzzy logic.
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 SiteTerrific Web Solutions

5. Neusciences
NCS provide leadingedge Intelligent Technology products and services
Over 10 years of experience developing and using neural nets and genetic algorithms in the real world Home Why we can help Solving problems Services ... Contact us
White Paper
October 2003
New release of NeuJDesk and first release of the JBi range of intelligent Java Beans
These releases include
  • Bayesian Classifier Case Based Reasoning Decision Tree Dynamic CMeans Fusion Unit GLS Regression Image Preprocessor KMeans Kohonen Multi-Layer Perceptron Principle Component Analysis
NeuJDesk Release 2
Release 2 adds 6 new versions to the existing 5 in Release 1. The 11 versions provide a wide range of intelligent processing and allow extensive trials within the NeuJDesk enviroment.
JBi Release 1
Release 1 of the JBi range of intelligent Java beans provides Java developers with an easy way to add new and exciting ways to process data to their products. These beans are the basis for the NeuJDesk range of products as well as other developments by Neusciences.

6. The MathWorks - Products - Fuzzy Logic Toolbox
The fuzzy logic Toolbox for use with MATLAB is a comprehensive tool for creation, visualization and evaluation of splines. fuzzy logic Toolbox 2.1.2.
Worldwide home store contact us site help create account my account ...
Get Pricing
Fuzzy Logic Toolbox 2.1.3
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. Full Product Description
Download Datasheet

Language Options: English Deutsch Italiano Trademarks

7. Welcome To Fuzzy Logic
Furry. Fuzzball 5.68. Staff list.
How to join Fuzzy Logic MUCK The wizards and administrators The rules and policies Links on and off of this page ***WE'VE MOVED***WE'VE MOVED***WE'VE MOVED***

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Last updated on 7 April 2003, at 2:35am Pacific Time.

8. Fuzzy Logic Jump Start.
fuzzy logic JUMPSTART PUBLICATIONS. fuzzy logic for Just Plain Folks (Online Book, Free for your personal use.). Most books about
FUZZY LOGIC JUMP-START PUBLICATIONS Fuzzy Logic for "Just Plain Folks" (Online Book, Free for your personal use.) Ch. 1 of 3. Fuzzy Logic - A Powerful New 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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9. Fuzzy Logic Laboratorium Linz - Hagenberg
Translate this page fuzzy logic Laboratorium, Softwarepark Hagenberg / Johannes Kepler Universität. Lectures, seminars,
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10. Neural Fuzzy Systems
1.1 An introduction to fuzzy logic 1.2 Operations on fuzzy sets 1.3 Fuzzy relations 1.3.1 The extension principle 1.3.2 Metrics for fuzzy numbers 1.3.3 Fuzzy
Neural Fuzzy Systems
5 credits, 30 h lectures + case study + examination. TCS, SE, A and IS. Neural fuzzy systems are artificial neural networks with fuzzy input/output information. The course consists of three parts: The first part surveys the most often used methods of approximate reasoning and fuzzy rule-based systems. The second part covers learning algorithms of feed-forward supervised multi-layer neural networks and Kohonen's algorithm for unsupervised learning. The third part includes learning algorithms of neuro-fuzzy networks. A large number of applications of neuro-fuzzy systems to diagnostics, control and decision support will be presented. Preliminary knowledge: Basics in numerical mathematics (e.g. the steepest descent method for minimization).
The lectures
Fuzzy systems
  • An introduction to fuzzy sets. Operations on fuzzy sets. (in pdf format)
  • Fuzzy relations (in pdf format)
  • Fuzzy implications (in pdf format)
  • The theory of approximate reasoning (in pdf format) ...
  • Fuzzy logic controllers. Effectivity of fuzzy systems. (in pdf format)
    Neural networks
  • The perceptron learning rule (in pdf format)
  • The delta learning rule (in pdf format)
  • The delta learning rule with semilinear activation function (in pdf format)
  • The winner-take-all learning rule. (in pdf format)
  • 11. Fuzzy Logic Tutorial - An Introduction
    org. fuzzy logic Tutorial. PART I Introduction to fuzzy logic INTRODUCTION; WHERE DID fuzzy logic COME FROM? WHAT IS fuzzy logic?
    SRS Home Front Page Monthly Issue Index
    Search WWW Search
    Fuzzy Logic Tutorial
    PART I - Introduction to Fuzzy Logic
    Author Information

    fuzzy logic AN INTRODUCTION. PART 1. Each article will include additional outside resource references for interested readers. WHERE DID fuzzy logic COME FROM?
    SRS Home Front Page Monthly Issue Index
    Search WWW Search
    FUZZY LOGIC - AN INTRODUCTION PART 1 by Steven D. Kaehler INTRODUCTION This is the first in a series of six articles intended to share information and experience in the realm of fuzzy logic (FL) and its application. This article will introduce FL. Through the course of this article series, a simple implementation will be explained in detail. Each article will include additional outside resource references for interested readers. WHERE DID FUZZY LOGIC COME FROM? The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership. This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement. Unfortunately, U.S. manufacturers have not been so quick to embrace this technology while the Europeans and Japanese have been aggressively building real products around it. WHAT IS FUZZY LOGIC?

    13. Fuzzy Logic Sources Of Information
    Maintained by Bob John, De Montfort University.
    Fuzzy Logic Sources of Information
    This source is constantly changing but is an attempt to draw as many of the sources of fuzzy logic information together. My personal fuzzy logic bibliography is available here - mainly references to type 2 and interval valued fuzzy sets but also some other relating to determining membership functions, some neural networks and some neuro fuzzy. Available online is a working paper on fuzzy inferencing systems which describes fuzzy inferencing systems and discusses many of the issues faced by fuzzy systems developers. I am particularly interested in type 2 fuzzy sets and have published some work in this area. Here is a description of my work on type 2 sets as well as more details about the activities of my PhD students.
    Workshops and Conferences
    Fuzzy Logic Workers
    Fuzzy Logic Tools and Companies ...
    Miscellaneous links
    If you have any sites of interest relating to any area of fuzzy logic that I can add please e-mail me on

    14. Stefano Pizzuti's Home Page
    ENEA, Italian agency for energy, new technologies and environment. Evolutionary computation, fuzzy logic, neural networks, chaos Theory and their application to energy related problems.

    15. Fuzzy Systems - A Tutorial
    It was Plato who laid the foundation for what would become fuzzy logic, indicating that there was a third region (beyond True and False) where these opposites
    by James F. Brule'
    Fuzzy systems is an alternative to traditional notions of set membership and logic that has its origins in ancient Greek philosophy, and applications at the leading edge of Artificial Intelligence. Yet, despite its long-standing origins, it is a relatively new field, and as such leaves much room for development. This paper will present the foundations of fuzzy systems, along with some of the more noteworthy objections to its use, with examples drawn from current research in the field of Artificial Intelligence. Ultimately, it will be demonstrated that the use of fuzzy systems makes a viable addition to the field of Artificial Intelligence, and perhaps more generally to formal mathematics as a whole.
    Natural language abounds with vague and imprecise concepts, such as "Sally is tall," or "It is very hot today." Such statements are difficult to translate into more precise language without losing some of their semantic value: for example, the statement "Sally's height is 152 cm." does not explicitly state that she is tall, and the statement "Sally's height is 1.2 standard deviations about the mean height for women of her age in her culture" is fraught with difficulties: would a woman 1.1999999 standard deviations above the mean be tall? Which culture does Sally belong to, and how is membership in it defined? While it might be argued that such vagueness is an obstacle to clarity of meaning, only the most staunch traditionalists would hold that there is no loss of richness of meaning when statements such as "Sally is tall" are discarded from a language. Yet this is just what happens when one tries to translate human language into classic logic. Such a loss is not noticed in the development of a payroll program, perhaps, but when one wants to allow for Šnatural language queries, or "knowledge representation" in expert systems, the meanings lost are often those being searched for.

    16. Sumeet Gupta's Neural Network Page
    Includes related fields like genetic algorithms, fuzzy logic, adaptive computing and complex systems and descriptions of each site.
    Sumeet's Neural Network Page
    Actually you will find much more than just Neural Nets here. Sites on related topics such as neuroscience, genetic algorithms,fuzzy logic, artficial life etc. have also been listed. Sorry but you will have to do the sorting yourself. You might want to visit my homepage Aberdeen Electronics Research Group - Look out for the Computational Intelligence Research and publications on neural networks and hybrid systems. Adaptive Simulated Annealing (ASA) : Code and related preprints. Lester Ingber. Alberta - Biological Computation Project : Research and references on the motion correspondence problem, the value unit connectionist architecture, network interpretation, redundant networks, medical diagnosis, and issues in cognitive science. Amsterdam - Intelligent Autonomous Systems Group - Robotics and Neurocomputing : Research papers, preprints, introductory textbook on neural networks. Antwerp - Born Bunge Foundation - Theoretical Neurobiology Unit : Realistic models of neurons to explore the function of the nervous system, particularly the mammalian cerebellum. Artificial Life Bibliography : Maintained by Ezequiel A Di Paolo at University of Sussex.

    17. FAQ: Fuzzy Logic And Fuzzy Expert Systems 1/1 [Monthly Posting]
    2 What is fuzzy logic? 3 Where is fuzzy logic used? 4 What is a fuzzy expert system? 8 Isn t fuzzy logic an inherent contradiction?

    18. 'Fuzzy' Logic
    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
    - 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.

    19. FAQ: Fuzzy Logic And Fuzzy Expert Systems 1/1 [Monthly Posting] - [2] What Is Fu
    2 What is fuzzy logic? J. Gen. Sys. 8. The original papers on fuzzy logic include Zadeh, Lotfi, Fuzzy Sets, Information and Control 8338353, 1965.
    [2] What is fuzzy logic?
    Go Back Up Go To Previous Go To Next

    20. Research Activities
    M. Sc. thesis by Tamer Samy download in .zip or .doc format.
    var cm_role = "live" var cm_host = "" var cm_taxid = "/memberembedded"
    Research * Download the contents of my M. Sc. thesis Fuzzy Logic Control For Power System Stabilization * Download my complete M. Sc. thesis Fuzzy Logic Control For Power System Stabilization Part-1 Fuzzy Logic Control For Power System Stabilization Part-2 Fuzzy Logic Control For Power System Stabilization Part-3 * This is my first international research , This research has been published in IASTED Power and Energy Systems Conference 2000 [All regards to my supervisors : Prof.A.A.Sallam and Dr.Kamel El-Serafi who helped me a lot] A NEAR-OPTIMAL FUZZY LOGIC STABILIZER FOR WEAKLY CONNECTED POWER SYSTEMS

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