In the mid 90s, when search tools (e.g.: Archie, Gopher, Veronica, Jughead) and search engines (e.g: Wandex, Aliweb, JumpStation, WebCrawler, Lycos, Excite, Infoseek, Inktomi, Northern Light, and AltaVista) were not yet as sophisticated and useful as search engines are today, it made sense to provide a list of links to specific topics to help people with similar interests. With this in mind, I maintained "Bruno Di Stefano's Research Homepage" at the University of Toronto, Faculty of Applied Science and Engineering, where I was an instructor within the Professional Development Program. A subset of "Bruno Di Stefano's Research Homepage" was "Bruno Di Stefano's Fuzzy Logic Links". Over the first 3 years it was visited in excess of 30,000 times. This page was relocated to the Nuptek's Web Site during early July 2001. From early July 2001 to December 2004 it has been visited in excess of 11,000 times. The contents changed continuously, tracking the coming and going of companies, research labs, publications, and people in the fuzzy logic community. By 2005, search engines became so good that the contents of this web page became less valuable and the number of visitors went down to one or two per day.
At the same time, people's interest in fuzzy logic changed. The heavy tail of a certain hype of the early 90s vanished. Many engineering practitioners made fuzzy logic part of their toolbox and started using it when warranted by the situation. To better understand this point, I suggest reading Fuzzy Logic zeitgeist, with statistics, by Kirk Zurell on Fri, 2007-06-29 15:34.
Because of all of the above considerations, the scope and nature of this web page will soon change. I will still provide a service in relations to fuzzy logic, together with other Computational Intelligence disciplines, but of different nature. For now, until further notice, this web page is still a collection of bookmarks, by its nature, constantly "under construction". I updates it as often as I can.
If you share my interests, accessing these links on a regular basis is probably a way to keep informed about new developments.
If you know of some URL that I should be pointing to, please, write to me.
Fuzzy Logic is a form of mathematical logic in which truth can assume a continuum of values between 0 and 1. It is a superset of conventional Boolean Logic that has been extended to handle the concept of partial truth. The many applications of Fuzzy Logic include, but are not limited to: automotive (i.e. ABS and cruise control), air conditioners, cameras, digital image processing, rice cookers,dishwashers, elevators, washing machines, video games, etc.
To know more about Fuzzy Logic, you can start by reading Fuzzy logic - Wikipedia, the free encyclopedia with all of its linked pages, particularlyPortal:Artificial intelligence - Wikipedia, the free encyclopedia.
At this point you can monitor regularly the newsgroups, Google Groups : Fuzzy Logic and comp.ai.fuzzy via Google . You can read the FAQs, the archived newsgroups of the past, and slowly work your way through the links of this web page.
Google Groups : Fuzzy Logic
Newsgroups, FAQs, & Archives
|Top - Home|
comp.ai.fuzzy via Google
FAQ: Fuzzy Logic and Fuzzy Expert Systems 1/1 [Monthly posting] - Introduction
FAQ: Fuzzy Logic and Fuzzy Expert Systems 1/1 [Monthly posting]
Fuzzy archive: By Thread - Jan 01 2001 - May 06 2002
Fuzzy archive by thread - 2000 - Fuzzy archive by thread - 1999 - Fuzzy archive by thread - 1998
Fuzzy archive by thread - 1997 - Fuzzy archive by thread - 1996 - Fuzzy archive by thread - 1995
Amazon.com: buying info: Fuzzy Thinking : The New Science of Fuzzy Logic
Amazon.com: buying info: Fuzzy Engineering
Amazon.com: buying info: An Introduction to Fuzzy Sets : Analysis and Design (Complex Adaptive Systems)
Fuzzy Logic - Computational Intelligence
An Introduction to Intelligent and Autonomous Control
Fuzzy Logic for Business, Finance and Management
Amazon.com: buying info: Applications of Fuzzy Logic: Towards High Machine Intelligence Quotient Systems
Amazon.com: buying info: Fuzzy Control
Amazon.com: buying info: Fuzzy Engineering
Prentice-Hall Academic -- 0131249916
Fuzzy Engineering, 1/e <BR>Kosko
Amazon.com: buying info: Fuzzy Logic and Neuro Fuzzy Applications Explained (Bk/Disk)
Amazon.com: buying info: Fuzzy Logic and Neurofuzzy Applications in Business and Finance
Amazon.com: buying info: Fuzzy Logic for Business, Finance, and Management (Advances in Fuzzy Systems, Vol. 12)
Amazon.com: buying info: Fuzzy Sets and Fuzzy Logic: Theory and Applications
Aptronix home page
Byte Craft Limited - Fuzzy Logic - Fuzzy Logic Resources
CRISP & FUZZY SYSTEMS ENGINEERING - FDM - FKB - FTA
HyperLogic's Home Page
E. Ruspini - Approximate Reasoning Foundations
Scianta Intelligence Technology Specialists Consultants
Fuzzy Logik Systeme GmbH
Genetica - Advanced Software Architectures S.r.l. - Home Page
Rigel's Home Page for 8051 and C166/ST10 - Fuzzy, Complete Fuzzy Logic Package for the 8051
The MathWorks - Fuzzy Logic Toolbox
Neuroforecaster Genetica Software Package
Ortech Engineering Inc. Web Site
Process Cybernetics Ltd. (Index)
Welcome to Rockwell Automation Fuzzy Logic
Togai InfraLogic, Inc.
Welcome to TransferTech
XpertRule Miner, the complete data mining solution
Welcome to FuzzyCLIPS --- FuzzyCLIPS --- Non-Commercial Download
Prof. Lotfi Zadeh's Papers
Prof. Ebrahim Mamdani's Papers
- Zadeh, L. A., "Fuzzy sets". Information and Control, Vol. 8, pp. 338-353. (1965).
- Zadeh, L. A., "The concept of a linguistic variable and its application to approximate reasoning". Information Sciences, Vol. 8, pp. 199–249, 301–357; Vol. 9, pp. 43–80. (1975).
- Zadeh, L. A., "Fuzzy Sets as a Basis for a Theory of Possibility". Fuzzy Sets and Systems, Vol. 1, No. 1, pp. 3–28 (1978).
Prof. Bart Kosko's Papers
- E. H. Mamdani, "Application of fuzzy logic to approximate reasoning using linguistic synthesis", Proceedings of the sixth international symposium on Multiple-valued logic, Logan, Utah, USA, Pages: 196 - 202. (1976 ).
Abstract (From ACM): "This paper describes an application of fuzzy logic in designing controllers for industrial plants. A Fuzzy Logic is used to synthesise linguistic control protocol of a skilled operator. The method has been applied to pilot scale plants as well as in a practical industrial situation. The merits of this method in its usefulness to control engineering are discussed. This work also illustrates the potential for using fuzzy logic in modelling and decision making. An avenue for further work in this area is described where the need is to go beyond a purely descriptive approach and explore means by which a prescriptive system may be implemented."
Full text available from ACM Digital Library
``Neural Fuzzy Agents for Profile Learning and Adaptive Object Matching,'' with Sanya Mitaim, Presence, vol. 7, no. 6, pp. 617-637, December 1998.
``Adaptive Joint Fuzzy Sets for Function Approximation,'' with Sanya Mitaim, Proceedings of the 1997 International Conference on Neural Networks
(ICNN-97), pp. 537-542, June 1997.
``Fuzzy Throttle and Brake Control for Platoons of Smart Cars,'' with Hyun Mun Kim and Julie Dickerson, Fuzzy Sets and Systems, vol. 84, no. 3,
209-234, 23 December 1996.
``What is the Best Shape for a Fuzzy Set in Function Approximation?,'' with Sanya Mitaim, Proceedings of the 5th IEEE International Conference on Fuzzy
Systems (FUZZ-96), pp. 1237-1243, September 1996.
A FUZZY ADAPTIVE LEARNING CONTROL NETWORK WITH ...
Journal of Accounting and Computers Issue 12 Article 1
The Semiotics of Control Rules: 'What Do You Mean by Positive Small?'
Technical Papers by F. Martin McNeill , PE, of Fuzzy Systems Engineering, and Dr. Michael O'Hagan of Fuzzy Logic, Inc.
Circuit Cellar Ink - Walter Banks
Circuit Cellar Ink - Constantin von Altrock
- Hamid R. Berenji and Pratap Khedkar, "Learning and Tuning Fuzzy Logic Controllers Through Reinforcements" IEEE Transactions on Neural Networks, Vol 3, No 5, Sept. 1992. pp. 724-740
Abstract (From IEEE): "A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing"
Full text available from IEEE Xplore
Ali A Afzalian Homepage
Personal Information on Marcello Chiaberge
Homepage Martine De Cock
Homepage of Nico du Bois
Roger Jang's Home Page
Prof. Dr. Etienne E. Kerre
Kevin M. Passino's Home Page
Alessandro Saffiotti Homepage
Henry L. Welch
About Fuzzy Systems Engineering - F.Martin McNeill, PE
Universities, Research Centres, Professional Societies, Journals, and Magazines
Fuzzy logic - Wikipedia, the free encyclopedia
Article#2 on Fuzzy Logic and Its Uses
Fuzziness and Probability
Fuzzy Logic Control
Fuzzy Information Systems
Fuzzy Logic Sources of Information
IGD-Department Visualization and Interaction Techniques
Index of /pub/SPS/MCU/fuzzy - Motorola Archive of Fuzzy Freeware
Lehre von J. Zhang
Dilemmas of Ambiguity and Vagueness
Examples of Vagueness that We live by
Paradoxes and Dilemmas
Vertices Wint94: Fuzzy Logic
An Introduction To Fuzzy Control Systems
Fuzzy Logic Overview
Fuzzy Logic Overview
Fuzzy Sets and Fuzzy Logic
Embedded Systems Programming Archive
FLLL - A brief course in Fuzzy Logic
Fuzzy Logic and Its Uses
Fuzzy Systems - A Tutorial
CONSTRUCTING FUZZY by William Siler, PhD, Birmingham, AL 35217, USA
Fuzzy Logic Tutorial - An Introduction