FUZZ-IEEE'96 Announcement

Wed, 24 Jul 1996 15:46:42 +0200



SEPTEMBER 8-11, 1996

Visit the FUZZ-IEEE Homepage at http://www.cis.cau.edu/fuzzy.htm

New Orleans is characterized by its balmy, sub-tropical climate. The French Quarter, famous for quaint buildings, narrow streets, patios and courtyards reflecting 18th century French and Spanish influences is one of many attractions that New Orleans has to offer.

Circle One: Dr. Mr. Mrs. Ms.

Last Name :
First Name :
Middle Initial :
IEEE Membership Number:
(Must be supplied to qualify for discount)

Mailing Address:
(Information to Appear on Badge)

First Name:
Last Name:

Conference Registration Fees:
After July 19, 1996
IEEE Members: $460
Non-Members: $510
Students: $130

Tutorial Registration Fees:
(Tutorials Sunday, September 8, 1996)
One Tutorial: $300
Two Tutorials: $450
Student-One Tutorial: $150
Student-Two Tutorials: $225
Indicate Tutorials Selected :
(Tutorial Registration is on a first-come, first-served basis)

Payment of Fees:
Please make check payable to FUZZ-IEEE'96 / or indicate credit
Payment(s) Enclosed:
Registration Fees:
Tutorial Fees:
Additional Proceedings:
Extra Banquet Ticket:
Grand Total: /
Credit Card#:
Expiration Date:
Name on Card:
Authorized Signature:
For Credit Card Registration Only, Fax to (714)-752-7444
Please mail completed Conference Registration form, along with
your payment to:

Meeting Management
2603 Main St., Suite 690
Irvine, CA 92714, USA
Email:MeetingMgt@aol.com /

Poydras at Loyola Avenue
New Orleans, Louisiana 70113-4141
Phone: (504) 561-1234 Fax: (504) 552-4210

This year's FUZZ-IEEE Conference is being held at the Hyatt Regency in
New Orleans, Louisiana. Located just minutes from the historic French
Quarter and the riverfront, the newly renovated Hyatt Regency,
New Orleans, captures the flavor of the Crescent City. The Hyatt Regency
is situated in the heart of the central business district near the New
Orleans Convention Center, Louisiana Superdome and the New Orleans Center
Shopping Mall. It is within 12 miles from the New Orleans International
Airport, 8 miles from Lakefront Airport and two blocks from Union Passenger
Terminal. Airport transportation is available through New Orleans Tours
outside the baggage claim area at a current rate of $10.00 per person,
each way. Attendees of the FUZZ-IEEE conference and guests of the Hyatt
will receive complimentary shuttle service of the Hyatt Regency New Orleans
to and from the hotel and French Quarter. The shuttle departs from the
lobby at regularly scheduled intervals throughout the day, and evening.
The hotel concierge will be happy to provide complete schedules of the
shuttle's route and departure times.

Your reservation must be received by August 11, 1996 to ensure
availability and special rates. Reservations received after that
date will be on a space availability basis.


Note: All guest rooms have one king size bed or two double beds.
There is an additional charge for roll-aways.

Rates (per night)

___ $133 US Single or Double Occupancy
___$153 US Triple Occupancy
___$173 US Quad Occupancy

Check applicable
___ $ 35 US Additional for Regency Level
___ $ 15 US Additional for Business Plan Level
___ Non-Smoking Room

Arrival Date/Time: / (Check in time is 3:00pm)
Departure Date: / (Check out time is 12 noon)

State -Zip:
Sharing Room with:
Special Requests: /
Enclosed is a check or money order for $

Enclosed is my credit card information authorizing my reservation to be guaranteed in the amount of $

Check applicable
American Express
Diners Club
Carte Blanche
Master Card

Credit Card #
Exp Date
Please print name as it appears on the card:

Reservations must be canceled 72 hours prior to arrival date for deposit
Reservations are subject to cancellation at 6pm if not guaranteed by credit

Contact Information:
General Chair:
Fredrick Petry
Center for Intelligent and Knowledge Based Systems
Tulane University
301 Stanley Thomas Hall
New Orleans, LA 70118
Phone: (504)865-5842
Fax: (504)862-8747
Email fuzz@cs.tulane.edu

Publicity Chairs:
Roy George, R. Srikanth
Clark Atlanta University
Atlanta, GA
roy@diamond.cau.edu /a
srikanth@diamond.cau.edu /a


Monday, September 9, 1996
8:15am - 9:00 am Plenary Session
9:00am - 9:30 am Break/ Posters
9:30am - 10:50 am Session 1
11:00am - 12:20 pm Session 2
1:35pm - 3:15 pm Session 3
3:15pm - 3:45 pm Break/Posters
3:45pm - 5:00 pm Session 4

Tuesday, September 9, 1996
8:15am - 9:55 am Session 5
9:55am - 10:25 am Break /Posters
10:25am - 12:05 pm Session 6
1:20pm - 3:00 pm Session 7
3:00pm - 3:30 pm Break/Posters
3:30pm - 5:00 pm Session 8

Wednesday, September 10, 1996
8:15am - 9:00 am Plenary Session
9:00am - 9:30 am Break /Posters
9:30am - 10:50 am Session 9
11:00am - 12:20 pm Session 10
1:35pm - 3:15 pm Session 11
3:15pm - 3:45 pm Break/Posters
3:45pm - 5:00 pm Session 12


Special Sessions
Fuzzy Modeling
Chair: Prof. Witold Pedrycz, University of Manitoba

Defuzzification Methods I
Chair: Dr. Dimitar P. Filev, Ford Motor Company

Defuzzification Methods II
Chair: Prof. Ronald R.Yager, Iona College

Optimization & Learning Transport
Chair: Dr. Piero Bonissone, CSIC

Defuzzification Methods I
Chair: Dr. Dimitar P. Filev, Ford Motor Company

Defuzzification Methods II
Chair: Prof. Ronald R.Yager, Iona College

Optimization & Learning Transport
Chair: Dr. Piero Bonissone, CSIC

Rough Sets I
Chair: Prof. Z. Pawlak, Warsaw University of Technology
Chair: Prof. T. Beaubouef, Xavier University

Rough Sets II
Chair: Prof. T.Y. Line, San Jose State University

FUZZY Engineering Design
Chair: Dr. Hans-Jurgen Sebastian,
International Institute for Applied Systems Analysis
Chair: Prof. Hans-Jurgen Zimmerman,
Aachen Institute of Technology

Learning in a FUZZY Framework
Chair: Dr. Bemadette Bouchon-Meunier,
Universite Pierre et Marie Curie

Advanced Methods of FUZZY Control
Chair: Dr. Hua Wang,
United Technologies Research Center

FUZZY Database Management Systems
Chair: Dr. Patrick Bosc, IRISA/ENSSAT



Sunday, September 8, 1996
8:00am - 12 noon

Tutorial #1 :
Imprecision Handling in Database Management and Information Retrieval Systems

Instructors: Gloria Bordogna, Patrick Bosc, Gabriella Pasi IRISA/ENSSAT, France
This tutorial is situated at the confluence of two important areas which are evolving very rapidly: the management of information on the one hand and the applications of fuzzy logic and possiblity theory to deal with imprecision and/or uncertainty on the other hand. Some basic notions regarding imprecision, uncertainty, graduality, databases and information retrieval (systems) are first recalled and the role of fuzziness in these systems is discussed. Concerning database management systems, two major aspects are examined:
i) how to represent, store and manipulate data tainted with imprecision and/or uncertainty, in particular in the framework of object -oriented modeling, and ii)the components and objectives of flexible queries (whose satisfaction is gradual rather than crisp). The last part of the presentation is devoted to the application of fuzzy sets in information retrieval systems for softening both document representation and query formulation.

Tutorial #2 :
Applications of Fuzzy Technology
Instructor: Xin Feng, Associate Professor,
Marquette University, Milwaukee, Wisconsin

This tutorial will provide fundamental concepts of fuzzy technology methodologies and address some of most concerned issues on how to successfully apply it to real world applications. Focus will be given to:
- latest on fuzzy technology applications,
- how to identify application areas suitable for fuzzy technology,
- how to select appropriate fuzzy technology tools,
- how to start their own projects,
- how to evaluate performance, and
-how to avoid unnecessary mistakes, what to do and what not to do, etc.
Also participants will learn first-hand experience from the instructor, including his successful stories and mistakes.
Tutorial Outline:
1) Introduction of fuzzy technology concepts
2) Fuzzy technology methodologies:
3) Application areas suited for fuzzy technology
4) Areas not suited for fuzzy technology
5) Survey of recent successful applications
6) Performance evaluation
7) Application examples developed by instructor
8) Cautions and Conclusion

Tutorial #3 :
An Introduction to Evolutionary Computation
Instructor: David B. Fogel, Natural Selection, Inc., La Jolla, California, USA
This tutorial will provide an introduction to the field of evolutionary computation, including genetic algorithms, evolution strategies, and evolutionary programming, and also genetic programming and artificial life. Examples of these procedures applied to the development of fuzzy and neural computing systems will be described.
Fundamental mathematical properties of the algorithms will be reviewed. Practical issues concerning the choice of representation, search operators and selection procedures will be discussed. Attendees of the tutorial will learn how to apply evolutionary algorithms to their own problems of interest.

1:00pm - 5:00pm

Tutorial #4 :
Fuzzy Logic in Pattern Recognition
Instructor: James M. Keller
University of Missouri-Columbia, Columbia, Missouri
Objective: The purpose of this tutorial is to survey various methodologies and aspects of pattern recognition and the role that fuzzy set theory and fuzzy logic can play in dealing with vague or imprecise features and data while making decisions. Both unsupervised and supervised approaches, along with training procedures, wherever
appropriate, will be discussed. Several examples and case studies will be presented in order to illustrate the advantages of a fuzzy approach to pattern recognition problems. Specific applications to problems such as automatic target recognition, handwritten character and word recognition, curve detection, and image segmentation will be developed. Participants will gain an understanding of the role of fuzzy set theory and fuzzy logic in pattern recognition, and will have specific information as to how to incorporate it into their own pattern recognition problems. A bibliography will be supplied to assist participants in finding the details of the algorithms
Target Audience: The target audience includes engineers, scientists, and academics who are faced with pattern recognition problems involving vague, imprecise, or incomplete information. Some familiarity with pattern recognition principles is necessary to appreciate the need for, and mechanisms for, the incorporation of fuzzy set theory into this domain.
(1) Automated decision making, including pattern recognition, has been utilized in a tremendous number of application areas for a long time. Some of the earliest successes of fuzzy set theory from an applications standpoint have been in the pattern recognition area (in particular, in clustering). There has been keen interest on
the part of researchers and practitioners to develop pattern recognition algorithms which deal with uncertainty that is not easily modeled by probability theory. Hence, this topic is of current relevance and interest. (2) There is a large body of knowledge generated in the insertion of fuzzy set theory into pattern recognition. While the research continues to expand, there are numerous accepted techniques which constitute a core of material for this subject.

1. What is pattern recognition?
2. What is the role of fuzzy methods in decision making
3. Supervised vs. Unsupervised Pattern Recognition
4. Clustering: crisp, fuzzy, and possibilistic.
5. Fuzzy rule-based approaches.
6. Crisp and fuzzy K-nearest neighbor algorithms.
7. Neural networks and fuzzy aggregation

Tutorial #5 :
Evolutionary Fuzzy Modeling: Theory and Control Applications
Instructors: Krishna Krishnakumar and A. Satyadas,
The University of Alabama, Tuscaloosa, Alabama

Evolutionary Algorithms (EA) have been shown to be powerful tools for synthesizing fuzzy models. Recent applications of Evolutionary Fuzzy Modeling (EFM) to aircraft and spacecraft control, process control, and fuzzy decision models for fault detection have demonstrated the strength of EFM in finding solutions for complex problems. This tutorial will provide a hands-on approach to problem solving using EFM. The tutorial is divided into: (a) Fuzzy primer; (b) EFM techniques and control applications.

Tutorial #6 :
Fundamentals of Fuzzy Logic Circuits
Instructor: Kaoru Hirota
Tokyo Institute of Technology, Japan
The fundamentals of fuzzy logic and fuzzy logical circuits are introduced first with several hardware implementation examples. Then the most important applications, i.e., fuzzy inference circuits, that are characterized as one of the fuzzy extension of combinatorial circuits in two valued Boolean logic are mentioned. In the case of human intelligence oriented fuzzy applications such as fuzzy inference. For such purposes the
concept of fuzzy flip flop has been introduced by the presenter who has more than 15 years career to guide company engineers in the field of understandable lecture even for the beginner of fuzzy technology, and audiences will be able to make design or develop various fuzzy hardware circuits toward the realization of fuzzy computers after finishing this tutorial.

Tutorial #7:
Fuzzy Control Theory: An Analytical Approach
Instructor: Hao Ying and Reza Langari
University of Texas Medical Branch, Galveston, Texas
Compared to the triumphant applications of fuzzy control, fuzzy control theory is far behind and still in its infancy. There is a growing need and effort in treating and studying fuzzy controllers in an analytical and rigorous manner rather than in the prevalent black-box and trial-and -error approach. In this tutorial, recent results on
various analytical aspects of fuzzy controllers relative to classic control theories will be presented. More specifically, the following topics will be covered: (1) fuzzy controllers (systems) as function approximators; (2) analytical structure of fuzzy controllers in relation to the classic controllers such as the PID controller; (3) general and limit structure of fuzzy controllers; (4) stability analysis of fuzzy control systems; and (5) adaptive fuzzy control. The mathematical insight demonstrated in the tutorial will broaden and deepen the participants' understanding on the peculiar characteristics and inherent nature of the fuzzy controllers.

					-Roy George (royg@cc.gatech.edu)