Table Of Content(NASA=-C?-10%901-Vol-1) PRICELEDNINGS TF THE N91-21775
SECOND JOINT TeCHNOLUGY wORESHOP ON NEUP AL --THAU--
NETWURKS AND FUZZY LOGIC, VOLUME 1 (NASA) NII-21785
240 p csct ogvF G3 Unclas
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NASA Conference Publication 16061
Proceedings of the Second
Joint Technology Workshop
on Neural Networks and
Fuzzy Logic
Volume |
Robert N. Lea and
James Villarreal, Editors
NASA Lyndon B. Johnson Space Center
Houston, Texas
Proceedings of a workshop sponsored by the
National Aeronautics and Space Administration,
Washington, D0 C., and cosponsored by
Lyndon B. Johnson Space Center and
the University of Houston, Clear Lake
Houston, Texas
April 10 - 13, 1990
NASA
National Aeronautics and
Space Administration
February 1991
Proceedings of the Second Joint Technology Workshop
on Neural Networks and Fuzzy Logic
Program
April 10, 1990 PM
6:00-8:00 Registration
Ap1r1,i 199l0 AM
7:30-8:00 Registration
8:00-8:30 Robert T. Savely, Branch Chief, Software Technology Branch, NASA/Lyndon 8B.
Johnson Space Center, Houston, TX, Welcoming Remarks .
8:30-9:00 Jon Erickson, Division Chief, Automation and Robotics Division, NASA/Lyndon B.
JohnSpasceo Cennte r, Houston, TX, Overview of Space Station
9:00-9:30 Barney Roberts, Planet Surface System Manager, NASA/Lyndon B. Johnson Space
Center, Houston, TX, Oveorf Mavrs/Liunare Iniwtiat ive
9:30-_9_: 4Br5ea k
9:45-10:45 Bernard Widrow., Stanford University, Neural Control Systems
10:45-11:45 Bart Kosko, University of Southern California, Los Angeles, CA, Fuzzy Sets and
Associative Memories
11:45-1:00 Lunch
Apr11,i 199l0 PM
1:00-1:45 Hal White, University of California at San Diego, Neural Network Representations
and Learning of Mappings and their Derivatives
1:45-2:15 Masaki Togai, Togai infralogic, inc., irvine, CA, impact of Application of Fuzzy
Thetoo Irnduystr y
2:15-2:30 Break
2:30-3:00 Takeshi Yamakawa, Kyushu Institute of Technology, lizuka, Fukuoka, Japan, Time-
Sweeping Mode Fuzzy Computer Hardware System --- Forward and Backward
Fuzzy InfereEnngicnee - --
3:00-3:30 P. Z. Wang, institute of Systems Science, National University of Singapore, The
Simplification of Fuzzy Control Algorithm and Hardware implementation
3:30-3__: 4Br5ea k
v
PRECEDING PAGE BLANK NOT FILME.
~ + s i
\y os
3:45-5:15 Lotfi Zadeh; University of California a: 3«’xe/ey, moderator of panel discussion on
Fuzzy Sets, Neural Networks, and intelligent Control
Panel members include:
Bernard Widrow, Stanford University
Bart Kosko, University of Southern California
Masaki Togai, Toga: infralogic Corporation
Takeshi Yamakawa, Kyushu Institute of Technology
P._ 2 Véang. Nationa! University of Singapore
Hal White, University of California, San Diego
Elie Sanchez, Neural and Fuzzy Systems Institute, Marseilles, France
Paul Werbos, National Science Foundation.
6:00-7:00 Wine Raned cCheeespe tion
7: K k
Professor Lotfi Zadeh: The Role of Logic in Human and Machine intelligence.
8:00-8:30 James Anderson, Brown University, Providence, Ri, Experiments with
Representations in Simple Neural Networks
8:30-9:00 James Bezdek, University of West Florida, Generalized Self Organizing Clustering
Schemes
9:15-9:45 Hiroyuki Watanabe, University of North Carolina, Chapel Hill, NC, A Single Board
Fuzzy Inference System
9:45-10:15 isao Hayashi, Central Research Laboratories, Matsushita Electrical industrial Co,
The Learning Function of NN-Driven Fuzzy Reasoning under Changes of Reasoning
Environment
*1 reak
19:30-11:00 Kaoru Hirota, Hosei University, Tokyo, Japan, A Solution 07 Inverse Problem of
Fuzzy Relational Equation by using Perceptron Model
11:00-11:45 Masaki Togai, Togai infralogic, inc., irvine, CA, Overview of LIFE (Laboratory for
international Fuzzy Engineering) Research
11:45-1:00 Lunch
12,1
1:00-1:30 James Keller, University of Missouri, Columbia, MO, Experiments on Neural
Network Architectures for Fuzzy Logic
1:30-2:00 John Yen, Texas A&M University, College Station, TX, Using Fuzzy Logic to
integrate Neural Networks and Knowledge-baSyssetedms
2:00-2:30 Hamid Berenji, Ames Research Center, Palo Alto, CA, An Architecture for
2:30-2:45 Break
2:45-3:15 Rod Taber, Center of Applied Optics, University of Alabama in Huntsville,
Huntsville, Alabama, Spatiotemporal Pattern Recognition with the Neuron Ring
3:15-3:45 Robert Shelton and James Villarreal, NASA/Lyndon 8. Johnson Space Center,
Houston, TX, Spatiotemporal Neural Networks
3:45-4:15 Yashvant Jani and Robert N. Lea, NASA/Lyndon 8. Johnson Space Center,
Houston, TX, Fuzzy Logic in Autonomous Spacecraft Operations
4:15-4:45 Kumpati (Bob) S. Narendra, Yale University, New Haven, CT, identification and
Control of Dynamical Systems using Neural Networks
4:45-5:15 Jacob Barhen, Center for Space Microelectronics Technology, Jet Propulsion
Laboratory, Pasadena, CA, Non-LipschitDyzniamaincs
Ap1r3,i 199l0 AM
8:00-8:30 Dan Greenwood, Netrologic, San Diego, CA, Diagnosis and Failure Prediction of
the Space ShuttlMaein Engine
8:30-9:00 Paul Werbos, Nationa! Science Foundation, Neural Nets for Control and the Link to
Fuzzy Logic
9:15-9:45 C.C. Lee, University of California at Berkeley, Berkeley, CA, An intelligent Control
System for Dynamic Processes
9:45-10:15 Ronald Yager, Machine intelligence institute, lona College, New Rochelle, NY, A
Neural Network Based Fuzzy Logic Controller
10: 15-10:45 Sankar K. Pal, NASA/Lyndon 8. Johnson Space Center, Houston, TX and indian
Statistical institute, Calcutta, india, Fuzzy Geometry, Entropy and image
Information
10;45-11;00__ Break
11:00-11:30 Enrique Ruspini, Stanford Research institute, Menlo Park, CA, The Semantics of
Fuzzy Logic
11:30-12:00 Robert Dawes, Martingale Research Corporation, identification, Estimation and
Control of Dynamical Systems with the Parametric Avalanche Neural Network
CONTENTS
An Architecture for Designing Fuzzy Logic Controllers Using Neural Networks
An Overview of the Neuron Ring Mode!
A Space - Time Neural Network
Fuzzy Logic n AutonOorbitmal oOpuerastio ns
identification and Control of Dynamical Systems Using Neural Networks
(Paper not provided by publicatidoatne )
Non-Lipschitzian Dynamics
(Paper not provided by publication date)
Space Shuttle Main Engine Fault Detection Using Neural Networks
Neurocoannd tFuzrzyo Llogi c: Connectanid oDensisgn s
An intelligent Control Based on Fuzzy Logic and Neural Networks
An Neural Network Based Fuzzy Logic Controller
(Paper not provided by publicatidoatne )
Fuzzy Geometry, Entropy and image information
The Semantics of Fuzzy Logic 233
identification, Estimation and Control of Dynamical Systems with the Parametric
Avalanche Neural Network 271
(Paper not provided by publication date)
viel
CONTENTS
VOLUME |!
Neural Control Systems 1 eae cereebeneunsees -enseequns
(Paper not provided by publication date)
ED cnc Wedel dae one adeueeuh daaeedeaeseneecesesesaians
Neural Network Representation and Learning of Mappings and their Derivatives
ee
impact of Application of Fuzzy Theory toindustry ..................... eee ee
(Paper not provided by publicatidoatne )
Time-sweeping Mode Fuzzy Computer a -- Forward and
BacFkuzzyw infaerernce dEng ine _.. , -
(Paper not provided by publicatidoatne )
The Simplification of Fuzzy Control Algorithm and Hardware implementation
‘eee 8 @
PANEL: Fuzzy Sets, Neural Networks, and Intelligent Control
PANEL MODERATOR: Lotfi Zadeh, University of California at Berkeley
PANEL MEMBERS: Bernard Widrow, Stanford University
Bart Kosko, University of Southern California
Masaki Togai, Togai infralogic Corporation
Takeshi Yamakawa, Kyushu Institute of Technology
P. Z. Wang, National Univ~-sity of Singapore
Hal White, University of California, San Diego
No papers were presented at this discussion.
Radar Signal CategorUsiingz aa Neutrail oNetnwo rk ............. wes 107
Self-organization and Clustering Algorithms iuiunueneedeet . M3
Single Board System for Fuzzy inference
Learning Control of inverted Pendulum System y Neural Network
Driven Fuzzy Reasorwng )
Solution of Inverse Problem of Fuzzy Relational Equation by sett
Perceptron Mode!
Overview of LIFE (Laboratory for international Fuzzy Engineering) Research 199
(Paper not provided by publication date)
Experiments on Neural Network Architectures for Fuzzy Logic 2c!
Using Fuzzy Logic to integrate Neural Networks and Knowledge-based Systems 217
Neura! Control Systems
(Paper not provided by publicatidaotne )
Kosko, b., NEURAL NETWORKS AND FUZZY SYSTEMS, Prentice-Hall, 1990
N91-21779
CHAPTER 17
FUZZY ASSOCIATIVE MEMORIES
Fuzzy Systems as Between-Cube Mappings
In Chapter 16, we introduced continuous or fuzzy sets as ports in the unit hypercube
I" = [0,1)}". Within the cube we were interested in the distance between points. This led
to measouf trhe esizse a nd fuzziness of a fuzzy set and, more fundamentally, to a measure
of how much one fuzzy set is a subset of another fuzzy set. This within-cube theory directly
extends to the continuoucass e whethre sepa ce X is a subseto f A" or, in general, where
X is a subset of products of real or complex spaces
The next step is to consider mappings between fuzzy cubes. This level of abstraction
provides a surprising and fruitful alternative to the propositional and predicate-calculus
reasoning techniques used in artificial-intelligence (Al) expert systems. It allows us to
reason with sets instead of propositions.
The fuzzy set framework is numerical and multidimensional. The Al framework is
symbolic and one-dimensional, with usually only bivalent expert “rales” or propositions
allowed. Both frameworks can encode structured knowledge in linguistic form. But the
fuzzy approach translates the structured knowledge into a flexible awmercal framework
and processes it im a manner that resembles neural network processing The numerical
framework also allows fuzzy systems to be adaptively inferred and modified, perhaps with
neuval or statistical techniques, directly from problem domaim sample data
