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   -> Volume 3, Issue 7


Meeting: UCLA Extension Short Course
 
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Watanabe, Nonie (NWatanab@UNEX.UCLA.EDU)
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PostPosted: Mon Dec 02, 2002 1:01 pm    
Subject: Meeting: UCLA Extension Short Course
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Meeting: UCLA Extension Short Course

UCLA Extension
Department of Engineering, Information Systems and Technical Management

announces a new short course

NONLINEAR DYNAMICS INFORMATION PROCESSING IN CHAOS, FUZZY LOGIC, AND
NEURAL NETWORKS
May 9-11, 1994 at UCLA

Researchers believe they have recently discovered the "Rosetta stone" of
chaos, fuzzy logic, and neural net research. This new artificial neural net
learning methodology can achieve learnable fuzzy logic reasoning at a
specific precision level, an understanding that becomes particularly powerful
for applications in communications and/or control when the inputs are
constrained by real-time nonlinear dynamical data. These new developments and
potential applications have led to the formation of Office of Naval
Research's multi-million dollar five-year core program to study nonlinear
dynamics spatiotemporal information processing (to begin FY '95).

This course discusses application of nonlinear dynamics such as collective
chaos for spatiotemporal information processing that has been embedded
through artificial neural networks (ANN), unifying fuzzy logic with ANN
through the bifurcation cascade of those chaotic outputs generated from
"piecewise negative logic neurons." These chaotic outputs define the fuzzy
membership triangle-shape function with a different degree of precision.
Another advantage of this methodology is the solving of nonlinear dynamics
for information processing in a synthetic nonlinear dynamical environment.
For example, ocean waves can be efficiently analyzed by nonlinear soliton
dynamics, rather than traditional Fourier series.

The course covers the elementary mathematics of nonlinear dynamics, the
bifurcation cascade route to chaos, rudimentary fuzzy logic, and essential
artificial neural network theory for those interdisciplinary participants
with only basic knowledge of the subject area. Various nonlinear dynamical
phenomena in chaos, fuzzy, and neural-like learning are illustrated in terms
of spatiotemporal information processing:
- signal/image de-noise
- control device/machine chaos
- communication coding
- biomedical applications.

Implementation techniques in massive and parallel artificial neural network
chips are given. The course also delineates the difference between the
classical sigmoidal function and the appropriate nonlinear element function
that generates chaos cascade outputs for the fuzzy membership function.

COORDINATOR AND LECTURER
Harold Szu, PhD
Research Physicist, Washington, D.C. Dr. Szu's current research involves
artificial neural networks, chips, fuzzy logic, chaos theory, wavelet
transforms, character recognition, and constrained optimization. Dr. Szu is
also a technical representative to ONR on neural networks and related
research, and has been engaged in plasma physics and optical engineering
research for the past 16 years. He holds six patents, has published more than
150 technical papers, plus two textbooks. Dr. Szu is a co-founder and
president of The International Neural Network Society, and an editor for the
journal Neural Networks.

Dates: May 9-11 (Monday through Wednesday)
Time: 8 am-5 pm
Location: Room 211, UCLA Extension Building, 10995 Le Conte Avenue (adjacent
to the UCLA campus), Los Angeles, California
Fee: $1095, includes lecture notes

For further information, call the Short Course Program Office at (310)
825-3344; or E-mail Marcus Hennessy at mhenness@unex.ucla.edu
All times are GMT + 1 Hour
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