Fuzzy Logic & Rough Sets Tutorial and Workshop (fwd)

T.Y. Lin (tylin@mathcs.sjsu.edu)
Thu, 18 Sep 1997 15:04:15 +0200 (MET DST)

> From MWILDBER@epri.com Mon Sep 15 17:46:38 1997
>
> >REMINDER: ---- Fuzzy Logic & Rough Sets Tutorial and Workshop
> >September 26, 1997
> >EPRI Headquarters
> >3412 Hillview Avenue
> >Palo Alto, California
> >
> >-----Description
> >Fuzzy logic reflects human reasoning about vague information and thus
> >provides new artificial intelligence techniques for systematic analysis of
> >qualitative data. The theory of fuzzy sets originated from the need to
> >handle logically the intentional vagueness characteristic of linguistic
> >variables ("rather large", "very small", etc.) as used in ordinary
> >conversation and in rhetorical arguments. Since fuzzy logic-based systems
> >can tolerate imprecision and can even utilize language-like vagueness to
> >smooth data gaps, they excel at providing robust control, failure diagnosis
> >or forecasting in situations where precise input is unavailable,
> >inappropriate, or too expensive. The concept of rough sets is complementary
> >to that of fuzzy sets. It attempts to handle logically the notion of
> >"indiscernibility" that arises from an inability to distinguish the
> >individuals in some a set with respect to all of their significant features.
> >The paradigm of rough sets defined over an approximation space can be
> >usefully applied to intelligent image filtering, voice recognition, rule
> >induction from data, and, especially, to data mining from multiple,
> >differently structured, databases.
> >
> >EPRI's Strategic Research and Development Group has applied fuzzy logic,
> >alone and in combination with techniques like neural networks, to turbine
> >startup and operation, multi-area power generation scheduling, and
> >intelligent dynamic control of power plants. Research is continuing into
> >other applications of fuzzy logic or rough sets.
> >
> >This tutorial-workshop is the sixth in a series on mathematical modeling and
> >computational methods with emphasis on electric power industry applications.
> >
> >-----Who Should Attend
> >This tutorial is of interest those who want or need to:
> >
> >* initiate or manage projects involving applications in control, diagnostics
> >or forecasting based on vague or imprecise data
> >* understand the advantages and limitations in the use of fuzzy logic or
> >rough sets for their applications
> >* become familiar with the state-of-the-art in these areas
> >
> >-----Registration
> >Attendance is open to all without fee, but prior registration is necessary.
> >Copies of written notes will be distributed to attendees. To register, send
> >the information requested in the form below to Terri Bekowies, P.O. Box
> >10412, Palo Alto, CA 94303-9743; phone (415) 855-2432, fax (415) 855-2287, or
> >e-mail: SRD@epri.com.
> >All sessions will be held in the EPRI Conference Center. Visitors should
> >first obtain badges from the reception desk in the Lobby of Building 1 before
> >proceeding to the Conference Center.
> >
> >-----Workshop Outline: Friday, September 26, 1997
> >The morning session, from 9:00AM to 12:00 noon, will be an elementary
> >tutorial, presented by Dr. Martin Wildberger, Manager of Mathematics and
> >Information Science in EPRI's Strategic Research and Development Group. The
> >tutorial will emphasize what practical uses may be made of the concepts of
> >fuzzy and rough sets. The following is a fuzzy/rough outline:
> >* Qualitative versus quantitative modeling
> >* Three kinds of uncertainty: randomness, vagueness and indiscernibility
> >* Handling vagueness with fuzzy sets & fuzzy logic
> >* Handling indiscernibility with rough sets
> >* Why use fuzzy logic? -- advantages
> >* Why not use fuzzy logic? -- disadvantages
> >* Using fuzzy sets combined with neural networks
> >* Using fuzzy sets combined with genetic algorithms
> >* Promising application areas for rough sets
> >
> >The afternoon session, from 12:45 to 3:30PM will include:
> >(1) a talk by Prof. T.Y. Lin of San Jose State University on applications of
> >Rough Sets
> >(2) a talk by Prof. John R. Clymer of California State University, Fullerton,
> >on a fuzzy, adaptive expert system controller that uses heuristics and
> >evolutionary algorithms to discover optimal decision making rules.
> >Application examples will include air traffic control and area vehicle
> >traffic control.
> >(3) a brief review by Dr. Wildberger of an EPRI research project in the
> >application of fuzzy logic to startup and load following for steam turbines
> >
> >A roundtable discussion on prospectives for further applications of fuzzy
> >logic or rough sets within the electric enterprise will commence at 3:30 p.m.
> >
> >---- For Additional Technical Content Information
> >E-mail: SRD@epri.com or call:
> >* Martin Wildberger, Manager (415) 855-1043
> >* John Stringer, Technical Executive (415) 855-2472
> >-----------------------------------------------------------------------------
> >------------------------------------
> >Fuzzy Logic & Rough Sets Tutorial Workshop, Palo Alto, CA, September 26, 1997
> >I am registering for the above tutorial and workshop.
> >
> >
> >Name
> >Title/Department
> >Organization
> >Address
> >City/State/Zip
> >Phone ( )
> >Fax ( )
> >Internet ID
> >Signature
> >Return this information to: Electric Power Research Institute, Attn.: Terri
> >Bekowies, P.O. Box 10412, Palo Alto, CA 94303-9743; e-mail: SRD@epri.com,
> >phone (415) 855-2432, fax (415) 855-2287.
> >
> >
> >A. Martin Wildberger, Ph.D.
> >Mgr., Mathematics & Information Science
> >Strategic Research & Development
> >Electric Power Research Institute
> >3412 Hillview Avenue
> >Palo Alto, CA 94304-1395
> >U.S.A.
> >E-Mail: mwildber@epri.com
> >Phone: (415) 855-1043
> >FAX: (415) 855-2287
> >
>
>