From crabapple.srv.cs.cmu.edu!honeydew.srv.cs.cmu.edu!das-news.harvard.edu!noc.near.net!howland.reston.ans.net!usc!elroy.jpl.nasa.gov!decwrl!netcomsv!netcom.com!iiscorp Thu Aug 19 15:46:34 EDT 1993 Article: 18440 of comp.ai Xref: crabapple.srv.cs.cmu.edu comp.ai:18440 comp.ai.neural-nets:11886 Newsgroups: comp.ai,comp.ai.neural-nets Path: crabapple.srv.cs.cmu.edu!honeydew.srv.cs.cmu.edu!das-news.harvard.edu!noc.near.net!howland.reston.ans.net!usc!elroy.jpl.nasa.gov!decwrl!netcomsv!netcom.com!iiscorp From: iiscorp@netcom.com (IIS Corp) Subject: A short course on Fuzzy Logic Inference Message-ID: Organization: NETCOM On-line Communication Services (408 241-9760 guest) Date: Wed, 18 Aug 1993 23:49:37 GMT Lines: 335 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 ** ********************************************************** 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: iiscorp@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: iiscorp@netcom.com ************************************************ 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 X------------------- (cut here) ----------------------- Registration Form Desired Location: ___ Washington D.C. ___ Los Angeles, CA Name: _________________________________________ Address: __________________________________________________ __________________________________________________ Business/Home Phone: _________________________ Fax:_________________ Course Fee: $1395 ($1295 for early registrants or per person for teams of three or more) ___ Check Enclosed ___ Money Order ___ Purchase Order ___ Billing authorization (enclosed) Credit Card payment: ___ Visa ___ Master Card ___ American Express Card #_________________ Expiration Date ____________________ Signature _____________________ Name on Card: (please print) ___________________________________ 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: iiscorp@netcom.com ______________________________________________________