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(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 a /o? 00054253 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