A short course on Fuzzy Logic Inference

A short course on Fuzzy Logic Inference

Post by IIS Co » Fri, 20 Aug 1993 08:13:30



               Intelligent Inference Systems Corp.

                         Presents

         Zadeh, Ruspini, Bezdek, Bonissone, and Berenji

                            On

                Fuzzy Logic Inference Systems

                   A Five Day Short Course

        Washington D.C.                 Los Angeles, CA
        Nov. 15-19, 1993                Nov. 29- Dec. 3, 1993

Introduced by Lotfi Zadeh, Fuzzy Logic methods may be used to
design intelligent systems utilizing knowledge expressed in natural
language.  This methodology, an important source of artificial
intelligence applications, permits the processing of both symbolic
and numerical information.  Fuzzy logic has been applied to control
trains (Sendai subway), elevators, household appliances, cameras, and
manufacturing processes.  Systems designed and developed utilizing
fuzzy-logic methods have been shown to be more efficient than those
based on conventional approaches.  In combination with Computational
Neural Networks techniques, fuzzy-logic methods may be used to design
robust adaptive control systems.  

This course discusses the application of fuzzy logic and neural
networks techniques to the design of fuzzy and hybrid neuro-fuzzy
systems.  More than 11 case studies will be discussed in detail.  At
the completion of this course, you will have a full understanding of
the benefits of this technology, will know  about existing successful
applications, and will develop the necessary understanding and
knowledge to design and apply fuzzy logic to your particular needs.

Who Should Attend:

Engineers, technical managers and project leaders, scientists,
systems analysts, as well as others who would like to have more
knowledge about this emerging technology.

About the course:

This is the strongest and most complete course
available on this topic. All the presenters of this course are
pioneers of the field, including Professor Lotfi Zadeh, who
introduced its seminal ideas and concepts.  Even if you have already
taken a course or  tutorial on fuzzy logic, you should try to attend
this course since it will provide you with a deeper understanding of
the latest techniques and applications in the areas of fuzzy logic,
soft computing, pattern recognition, intelligent control,
computational neural networks, and adaptive neuro-fuzzy systems.

Hands-on and Problem Solving Sessions:

Include presentations by commercial tool developers on their available
software and hardware products for fuzzy logic.

**********************************************************
**     Spend 5 days with the pioneers of Fuzzy Logic    **
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Instructors:

Lotfi Zadeh, Ph.D. is the inventor and the "father of fuzzy logic".  
He has been on the faculty of Electrical Engineering departments at
the Columbia University and University of California, Berkeley.  He is
now a Professor Emeritus and the director of the UC Berkeley's
initiative on Soft Computing.  He has won numerous awards including
the Paul-Sabatier University Honorary Doctorate in 1986, Japan's
Honda Award in 1989, IEEE Education Medal in 1973, IEEE Centennial
Medal in 1984, and IEEE Richard W. Hamming Medal in 1992.

Hamid R. Berenji, Ph.D.   is a senior research scientist at the
Artificial Intelligence Research Branch of NASA Ames Research
Center in Moffett Field, California.  He is the principal investigator
of the research project on intelligent control, and was a program
chairman for the IEEE International Conference on Neural Networks
(ICNN'93) conference in San Francisco.  He serves on the editorial
board of several technical publications including as an associate
editor for IEEE transactions on Fuzzy Systems and IEEE transactions
for Neural Networks.  He is a program cochairman for the 1994
IEEE conference on Fuzzy Systems, Orlando, Florida.

Enrique Ruspini, Ph.D. is a senior computer scientist and a SRI
Fellow at the Artificial Intelligence Center of SRI International in
Menlo Park, CA.  He has many years of experience in research in the
theory and applications of fuzzy logic.  He was the General Chairman
of the IEEE International Conference on Fuzzy Systems (FUZZ-
IEEE'93) and the IEEE International conference on Neural Networks
(ICNN'93).  He is a Fulbright Fellow, one of the founders of the North
American Fuzzy Information Processing Society, and a recipient of
that society's King-Sun Fu award.  He is the Program Cochairman for
the 1994 IEEE conference on Fuzzy Systems, Orlando, Florida.

Jim Bezdek, Ph.D. currently holds an Eminent Scholar Chair with
the Department of Computer Science at the U. of W. Florida. His
research interests include pattern recognition, computational neural
networks, image processing and machine vision, medical computing,
and expert systems.   He is the founding editor of the Int'l Jo. of
Approximate Reasoning and the IEEE Trans. Fuzzy Systems; and is an
associate editor of the: IEEE Trans. NN, and Int'l Journals of Applied
Intelligence, General Systems, and Fuzzy Sets and Systems. He is a
past president of IFSA  (Int'l Fuzzy Systems Assoc.) and NAFIPS
(North American Fuzzy Information Processing Society), and has
been an ACM national lecturer for the 1990-93 program years. Dr.
Bezdek is a fellow of the IEEE.

Piero Bonissone, Ph.D. is a senior Computer Scientist with the
Corporate Research and Development Center in Schenectady, NY, and
an Adjunct Professor of Electrical, Computer and Systems
Engineering at RPI.  He has published numerous papers on
approximate reasoning, fuzzy sets, pattern recognition, and expert
systems.  He is also a recipient of North American Fuzzy Information
Processing Society's King-Sun Fu award.  He was the program
chairman of the FUZZ-IEEE'93 conference.   He is the general
chairman for the 1994 IEEE conference on Fuzzy Systems, Orlando,
Florida.

*****************************************************************

Course Certificate:

Each student in the course will receive a personalized certificate
indicating that he or she has completed this course taught by the
pioneers of the field of fuzzy logic: Lotfi Zadeh, Enrique Ruspini,
Jim Bezdek, Piero Bonissone, and Hamid Berenji

Additional Information:

Contact Intelligent Inference Systems Corp., Phone (408) 730-8345,
Fax: (408) 730-8550 or send an electronic mail to: iisc...@netcom.com

Benefits:

Learn how to design and apply fuzzy logic, understand the fundamentals
of this field, learn about the most recent developments, learn about
available software and hardware tools for fuzzy logic, and explore the
wide range of applications of fuzzy logic (more than 11 case studies
will be discussed)

Intelligent Inference Systems (IIS Corp.) specializes in technical
training and consulting in fuzzy logic, fuzzy control, neural
networks, and knowledge-based systems.  In-house courses are also
offered.  For further information on arranging an in-house
course, contact:

Intelligent Inference Systems Corp.
P.O. Box 2908,
Sunnyvale, CA 94087.
Phone : (408) 730-8345
Fax: (408) 730-8550
email: iisc...@netcom.com

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                      Course Outline

Classes: 8:30 a.m. to 4:30 p.m.
Hands-on and Problem Solving Sessions: 4:30 p.m. to 6:00 p.m.

Day 1. Fundamentals of Fuzzy logic -- Enrique Ruspini

* Fuzzy sets vs. crisp sets * Role of fuzzy sets in uncertainty
management * Why fuzzy logic is needed? * Fuzzy logic vs.
probability theory * Products based on fuzzy logic * Status of fuzzy
computer chips * Calculus of If-Then rules * Approximate reasoning
methods * Motivations for fuzzy logic * Fuzzy sets * Fuzzy set
operations * Alternative combination operators * Fuzzy relations
and mappings * The extension principle Fuzzy inferential methods *
Representation of approximate rules * Generalized modus ponens *
Possibility Theory * Translation rules * Understanding Fuzzy Logic
* Possibility distribution as elastic constraints * Case Study 1:
Mobile robot motion control * Case Study 2: Flexible arm manipulator

Day 2: Advanced  Fuzzy Logic and Intelligent Control --
Lotfi Zadeh, Hamid Berenji

Taxonomy and interpretation of If-Then rules * Rules with
exceptions and qualifications * Analysis of collections of Fuzzy If-
Then Rules * Use of FA-Prolog * Algebraic operations on Fuzzy If-
Then Rules * Computing with fuzzy probabilities * Induction of
Fuzzy If-Then Rules from data * Relations with Neural Networks *
Fundamentals of Intelligent Control * Artificial Intelligence and
control Hierarchical control * Learning control systems * Fuzzy
logic control * Designing  a fuzzy logic controller * Knowledge
Representation in fuzzy logic control * Fine-tuning a fuzzy logic
controller * Case study 3: Cart-pole balancing * Case study 4:
Fuzzy parking control * Applications of fuzzy logic control * Case
study 5: Automatic train control * Case study 6: Helicopter
control * Fuzzy logic hardwares and computer chips * Fuzzy logic
software tools * Fuzzy system analysis * Fuzzy system
identification * Structure identification of FLCs * Stability
analysis of FLCs

Day 3: Adaptive Fuzzy and Neural Network Systems --
Hamid Berenji

Computational Neural Networks *  Recurrent neural networks *  
CMAC architectures *  Hybrid neural network and fuzzy logic
controllers * Fuzzy logic control and backpropagation * Fuzzy logic
control and reinforcement learning * Approximate Reasoning-based
Intelligent Control (ARIC): * Single-layer neural networks (ARIC
architecture) * Multi-layer neural networks (GARIC architecture) *
Guiding reinforcement learning with fuzzy logic *  Generating linear
fuzzy rules from data using radial basis functions * Case study 7:
GARIC applied to cart-pole balancing Case study 8: Space
Shuttle attitude control with fuzzy logic and reinforcement learning

Day 4: Approximate Reasoning in Knowledge-based systems
-- Piero Bonissone

Introduction to Knowledge Based Systems (KBS) * Topology of
Approximate Reasoning Systems * Bayesian Network * Fuzziness in
probabilistic systems * Practical Considerations for
implementation * Dempster Shafer (Belief) Theory  * Fuzziness in
Dempster-Shafer Theory * Reasoning with uncertainty in rule based
systems * PRIMO: A plausible reasoning system * Knowledge
Representation * Multi-valued logics: Triangular norms and conorms
* Control of Inference * Case study 9: Use of a Fuzzy Rule Based
System in ASW Application * Software Engineering for KBS and FLC
* Comparison of FLC with Classical Controller * A Software
Perspective to FLCs * FLC Development Phase * Knowledge
Representation * Compatibility Relations and Modus Ponens *
Inference Process in FLCs * FLC Compilation and Run-time Phases *
Case study 10: Example of Compilation and Run-time system for
Fuzzy PI

Day 5: Numerical Pattern Recognition  -- Jim Bezdek

Pattern Recognition * Object Data * Relational Data * Labeled Data
* Clustering & Classification with Fuzzy Model * Partition Spaces    
* Case Study 11: Fuzzy c-Means  * Applications Vignettes *
Advanced Topics and Applications  * Relational Clustering *
Properties of * Fuzzy Relations * Fuzzy Similarity Relation Spaces
* Decomposition of Transitive Closures * SAHN Clustering
Algorithms  * Convex Decompositions * Objective Function
Approaches * Fuzzy Logic and Clustering Networks * Prototypes and
Re-labeling in Clustering * Sequential Hard c-Means * Kohonen's
LVQ and KSO Models * Generalized LVQ * Fuzzy LVQ * Fuzzy Logic
and Classifier Networks   * Biological Neural Models *
Computational Neural Networks * Feed Forward Classifier Networks  
* Statistical Decision Theory  

General Information:

IIS Corp. accepts registrations irrespective of race, creed, sex,
color, physical handicap, and national or ethnic origin.  This
includes but it is not limited to admissions, employment, and
educational services

Registration Fee:

$1395   Includes tuition, a full copy of the notes, continental
        breakfasts, and refreshments during the breaks

$1295   Per person for teams of three or more from the same organization.

Save an additional $100 when registering by September 20, 1993.  
A $75 processing fee is charged if registration is cancelled before
October 25, 1993.  No refund after October 25, 1993 but substitution
is allowed at all times.

Locations and Accommodations:

Please arrange accommodation directly with the hotel.  Special rates are
available by mentioning "IIS Corp. Fuzzy Logic Course".

For Washington D.C. Course:             For Los Angeles Course:

Sheraton Crystal City Hotel             Century Plaza Hotel and Towers
1800 Jefferson Davis Highway            2055 Avenue of Stars
Arlington, Virginia 22202               Los Angeles, CA 90067
Telephone: (800) 862-7666               Telephone: (310) 551-3300

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Please mail, fax, or email this form to:

              Intelligent Inference Systems Corp.
              P.O. Box 2908
              Sunnyvale, CA 94087
              Phone: (408) 730-8345
              Fax: (408) 730-8550
              email: iisc...@netcom.com
______________________________________________________