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. http://www.abo.fi/~rfuller/fuzs.html

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. comp.ai.fuzzy 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. http://www.ortech-engr.com/fuzzy/reservoir.html

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 . http://plato.stanford.edu/entries/logic-fuzzy/

version history HOW TO CITE THIS ENTRY 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 SEP 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. http://www.austinlinks.com/Fuzzy/

The Net's Original Fuzzy Logic Archive - Since 1994 Basics Beverly's Fuzzy Logic Bookstore Brief Course in Fuzzy Logic comp.ai.fuzzy 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 WebMina@austinlinks.com SiteTerrific Web Solutions

5. Neusciences NCS provide leadingedge Intelligent Technology products and services http://www.neusciences.com/

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 Home Why we can help Solving problems Services Products and technologies ... Contact us White Paper Which do I use? Choosing an intelligent software algorithm 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. Learn more about NeuJDesk Download the NeuJDesk range Pricing for the NeuJDesk range 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. http://www.mathworks.com/products/fuzzylogic/

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 Tradeshow: SIAM Annual Meeting 2004 Demos available at upcoming trade shows Language Options: English Deutsch Italiano Trademarks

7. Welcome To Fuzzy Logic Furry. Fuzzball 5.68. Staff list. http://www.lionking.org/~beshon/fuzzylogic/

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*** VISIT FUZZY LOGIC BY TELNET: fuzzylogic.sytes.net 1701 Log in as a guest for your visit by typing: connect guest guest This FurRing site is owned by Beshon Click for the [ Next Site Skip a Page Random Page List ring pages Want to join the ring? go to the FurRing Home Page! Beshon 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 http://www.fuzzy-logic.com/

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 comments or suggestions Counter provided courtesy of digits.com

9. Fuzzy Logic Laboratorium Linz - Hagenberg Translate this page fuzzy logic Laboratorium, Softwarepark Hagenberg / Johannes Kepler Universität. Lectures, seminars, http://www.flll.uni-linz.ac.at/

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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 http://www.abo.fi/~rfuller/nfs.html

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? http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html

SRS Home Front Page Monthly Issue Index Search WWW Search seattlerobotics.org Fuzzy Logic Tutorial PART I - Introduction to Fuzzy Logic INTRODUCTION WHERE DID FUZZY LOGIC COME FROM? WHAT IS FUZZY LOGIC? ... Author Information

12. FUZZY LOGIC - AN INTRODUCTION fuzzy logic AN INTRODUCTION. PART 1. Each article will include additional outside resource references for interested readers. WHERE DID fuzzy logic COME FROM? http://www.seattlerobotics.org/encoder/mar98/fuz/fl_part1.html

SRS Home Front Page Monthly Issue Index Search WWW Search seattlerobotics.org 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. http://www.cse.dmu.ac.uk/~rij/fuzzy.html

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 Journals 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 rij@dmu.ac.uk

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. http://erg87067.casaccia.enea.it/stefano

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 http://www.austinlinks.com/Fuzzy/tutorial.html

FUZZY SYSTEMS - A TUTORIAL by James F. Brule' INTRODUCTION 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. THE PROBLEM: REAL-WORLD VAGUENESS 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. http://www.geocities.com/sumeet_gupta/neural.html

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? http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq.html

Introduction [1] What is the purpose of this newsgroup? [2] What is fuzzy logic? [3] Where is fuzzy logic used? Introduction [1] What is the purpose of this newsgroup? [2] What is fuzzy logic? [3] Where is fuzzy logic used? ... [24] Where to send calls for papers (cfp) and calls for participation

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 http://cati.csufresno.edu/upda/95/winter/story1.html

- 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. http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq-doc-2.html

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20. Research Activities M. Sc. thesis by Tamer Samy download in .zip or .doc format. http://tamersamy.tripod.com/research.htm

var cm_role = "live" var cm_host = "tripod.lycos.com" 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 http://www.iasted.com

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