Fuzzy Logic Links

(Page of Historical Interest Only)

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.

Road-Map to Fuzzy Logic

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.

Newsgroups, FAQs, & Archives

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Companies & Products


Prof. Lotfi Zadeh's Papers

Prof. Ebrahim Mamdani'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

Prof. Bart Kosko's Papers

``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.


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

1992 Papers

    • 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