simulation@uflorida.cis.ufl.edu (Moderator: Paul Fishwick) (04/24/91)
Volume: 21, Issue: 5, Tue Apr 23 14:20:15 EDT 1991 +----------------+ | TODAY'S TOPICS | +----------------+ (1) WANTED: Source Code for Distributed Simulation Example (2) BOOK: Continuous System Modeling (3) Combined Discrete/Continuous Methods (4) Dynamic Simulation (5) BOOK: Simulation and Statistics (6) PROGRAM: Neural Nets in Systems, Control, Vision and Genetics * Moderator: Paul Fishwick, Univ. of Florida * Send topical mail to: simulation@bikini.cis.ufl.edu OR post to comp.simulation via USENET * Archives available via FTP to bikini.cis.ufl.edu (128.227.224.1). Login as 'ftp', use your last name as the password, change directory to pub/simdigest. Do 'type binary' before any file xfers. * Simulation Tools available by doing above and changing the directory to pub/simdigest/tools. ----------------------------------------------------------------------------- To: uunet!comp-simulation@uunet.UU.NET Path: cs.utexas.edu!helios!dcook From: dcook@neuron.tamu.edu (David A Cook tcsh) Newsgroups: comp.simulation Subject: Distributed Simulation Date: 10 Apr 91 16:42:18 GMT Sender: usenet@TAMU.EDU Distribution: comp.simulation Organization: Texas A&M University, College Station, Texas To: comp-simulation@cs.utexas.edu I am looking for the source code for a simple distributed simulation example. I am working on my dissertation, and want to port a simple example to my system here, run it, and then modify it to a different type of message passing. Anybody out there got any, or know where I can find some? Thanks. David A. Cook ------------------------------ Date: Wed, 10 Apr 1991 18:16:48 MDT From: CELLIER@ECEVAX.ECE.ARIZONA.EDU Subject: Book Announcement ... book will be out in early May To: Fishwick@Fish.CIS.UFL.Edu X-Vmsmail-To: IN::"Fishwick@Fish.CIS.UFL.Edu" Announcing a systematic, broad-based approach for engineers, scientists, and applied mathematicians - F. E. Cellier, University of Arizona, Tucson, AZ CONTINUOUS SYSTEM MODELING Modeling and simulation have become indispensable design tools since they permit us to predict the behavior of a system before it is actually built. In fact, modeling and simulation are the only techniques available that allow us to analyze arbitrarily nonlinear systems accurately and under varying experimental conditions. Cellier's CONTINUOUS SYSTEM MODELING introduces students to an important subclass of modeling. It deals with the modeling of systems described by a set of ordinary or partial differential equations or difference equations. Simulation will be treated in a later volume. Cellier's aim in this first book is to provide students and teachers with a unified framework for the methodology of modeling nonlinear systems described by differential and difference equations. It introduces many of the major modeling techniques and software tools which support the modeling of any continuous system. The coverage culminates by addressing the modeling of the process of modeling itself, i.e., how can this process be formulated and formalized for implementation by artificial-intelligence methods such as neural networks. Because modeling is used in many branches of the hard and soft sciences, a diversity of terminologies and application-specific jargon has proliferated. This book attempts to unify the entire area by explaining the similarities and dissimilarities between various techniques and by creating a common toolbox using a common terminology. The text has the flavor of the mathematical discipline of dynamical systems and is strongly oriented toward Newtonian physical science with the concepts of mass and energy and the laws of thermodynamics central to the discussion. It provides detailed coverage of modeling techniques such as bond graphs and system dynamics, and various software tools such as DYMOLA and STELLA. Each chapter of this senior and graduate-level text is fully complemented with a summary, references, homework problems, and suggestions for projects and research. Professor Cellier will make available a solutions manual with accompanying brief tutorial on a 5 1/4 in. diskette. CONTENTS: Preface. 1. Introduction, Scope, Definitions. 2. Basic Principles of Continuous System Modeling. 3. Principles of Passive Electrical Circuit Modeling. 4. Principles of Planar Mechanical System Modeling. 5. Hierarchical Modular Modeling of Continuous Systems. 6. Principles of Active Electrical Circuit Modeling. 7. Bond Graph Modeling. 8. Modeling Non-Equilibrium Thermodynamics. 9. Modeling Chemical Reaction Kinetics. 10. Population Dynamics Modeling. 11. System Dynamics. 12. Naive Physics. 13. Inductive Reasoning. 14. Artificial Neural Networks and Genetic Algorithms. 15. Automated Model Synthesis. 1991/755 pp., 288 illus./Hardcover/$69.50 ISBN 0-387-97502-0 ========================================================================== APPROVAL COPY REQUEST/ORDER FORM ========================================================================== Yes, please send me: _____ copy(ies) of CONTINUOUS SYSTEM MODELING, F.E. Cellier/97502-0/$69.50 [ ] Approval Copy Course Title______________________________________________________________ Starting Date____________________________________Enrollment_______________ Text Decision Date________________________________________________________ Text Now in Use___________________________________________________________ [ ] Purchased Copy CALL TOLL FREE: 1-800-SPRINGE(R) [1-800-777-4643] in NJ, 201-348-4033 [9am to 4:30pm EST] [NY, NJ, MA, and CA residents, please add sales tax. Canadian residents please add 7% GST. Prepayment required from individuals. Please add $2.50 for shipping.] TOTAL ENCLOSED:____________________ PAYMENT: [ ] Check Credit Card: [ ] VISA [ ] MC [ ] AMEX [ ] DISCOVER Card Number:________________________________________Exp. Date:____________ Signature:__________________________________________Date:_________________ (Personal checks and money orders are also acceptable.) Name:_____________________________________________________________________ Address___________________________________________________________________ __________________________________________________________________________ City/State/Zip____________________________________________________________ 30-DAY APPROVAL POLICY I understand that the text I have requested for examination will be send to me on a 30-day approval basis. If I decide to adopt, I will return the invoice and indicate the course, name of the bookstore ordering, and estimated order size. At that time, the invoive charges will be cancelled. Otherwise, I will either purchase the copy or return it to cancel charges. Signature_________________________________________________________________ Return to: Springer-Verlag New York, Inc. ATTN: Ken Quinn 175 Fifth Ave. New York, NY 10010 ------------------------------ Date: Thu, 11 Apr 91 15:23:24 +0200 From: Jean-Claude Pascal <jcp@droopy.laas.fr> To: simulation@bikini.cis.ufl.edu Subject: Information wanted hi -- The following question/inquiry might have been asked to this group before, but as a new user I do not have any knowledge of it. I will appreciate any information/help I can get. I am looking for some information/references/book titles on modelling and simulation of hybrid systems i.e. systems simultaneously combining discrete and continuous aspects. I'ld apreciate any information mailed to me, I'll post a summary of them on the net. Thank you very much in advance. Jean-Claude Pascal LAAS-CNRS, Toulouse, France ---------------------------------------------------------------------------- my e-mail address: jcp@laas.laas.fr ---------------------------------------------------------------------------- ------------------------------ Date: Thu, 11 Apr 91 10:08:52 -0600 From: kma%nil@hellgate.utah.edu (Kwan-Liu Ma) Subject: "dynamic" simulation Distribution: world Organization: Department of Computer Science, University of Utah Keywords: To: comp-simulation@hellgate.utah.edu Hi, I am interested in knowing if there is existing work or project under way targeting on "dynamic" simulation. Here "dynamic" means the ability to control `almost' all levels of your simulation. In fact, in my world, we call it "steering" computations instead of dynamic simulation. I am particularly working on scientific applications. The ability to steer your computations requires: ways to monitor your simulation states -> graphics (visualization!) ways to dynamically modify simulation parameters; For example, in the simulation of thunderstorm, one might want to modify wind speed, etc. and others. One advantage of steering is you don't have to restart your simulation every time when some simulation parameters must be changed. Apparently, there are many other advantages ... If you know of any work or projects somewhat related to what I am talking about here, please let me know via email. Or if you would like to discuss this subject with me, also send email to kma@nil.utah.edu. I will post a summary if there is interesting discussion or information. PS: Ohio Supercomputer Center has done some related work!! Kwan-Liu Ma Computer Science University of Utah kma@nil.utah.edu (801) 581 - 6678 ------------------------------ From: KUBVX1::KLEIJNEN "Jack Kleijnen, Tilburg, Netherlands" 6-MAR-1991 To: IN%"JSWAIN@umiami.IR.Miami.EDU" CC: KLEIJNEN Subj: RE: e-mail directory "Simulation and Statistics: an Introduction" by Jack P.C. Kleijnen & Willem J.H. van Groenendaal >From the Preface: This book is meant for those readers who wish to acquire a basic know- ledge of simulation. It gives a survey of problems that can be ana- lyzed by means of simulation, especially problems in economics, busi- ness administration, management science, operations research, and mathematical statistics. It also shows how to analyse simulation re- sults. This analysis makes it possible to obtain more general conclu- sions, when using simulation. Moreover, the efficient design of simu- lation experiments is discussed. For all these topics there is specia- lized literature, but this literature is often too difficult for the novice. After reading this book, however, the specialized literature becomes accessible. Thus this book prepares the readers for the appli- cation of the simulation technique in practice. Therefore the problems and pitfalls of practical applications are outlined. Understanding the examples discussed in this book, requires a basic knowledge of busi- ness administration and operations research. Understanding the tech- niques presented in this book, requires knowledge of elementary com- puter science, mathematics, and statistics. Hasty readers might skip some chapters. Chapter 2 evaluates pseudo- random number generators. Practitioners, however, often rely on the generator implemented on the available computer. Chapter 3 shows how pseudo-random numbers are transformed in order to sample from statis- tical distributions such as the normal and the Poisson distributions. In practice, there are subroutines for sampling from classic distri- butions. So hasty readers might skip chapters 2 and 3. Chapter 4 is important for readers interested in economic and business applica- tions. This chapter also covers Forrester's Industrial Dynamics or System Dynamics. Chapter 5 covers classic Operations Research applica- tions such as inventory and queuing systems. Chapter 6 presents com- puter programs for more complicated queuing systems, and briefly dis- cusses special simulation languages. Chapter 7 is an introduction to the next chapters. It shows how simulation is applied in mathematical statistics, for example to investigate the robustness of regression analysis. The next two chapters set this book apart from other text- books on simulation. Chapter 9 shows how statistical designs such as 2 designs can be applied to select the combinations of factors (parameters and variables) to be simulated. The results of such expe- riments are analyzed through Analysis Variance (ANOVA). Chapter 8, however, presents ANOVA in the more familair terms of regression ana- lysis. This leads to so-called metamodels, namely regression models of the input/output behavior of simulation models. Because the specifica- tion of the metamodel determines the experimental design, regression models are presented before experimental designs; see the chapters 8 and 9. Chapter 10 answers the question: given the experimental design of chapter 9, how long should the simulation program run? In other words: how many years or how many customers should be simulated to obtain accurate answers? To improve the accuracy this chapter also gives a few variance reduction techniques, such as common and antithe- tic random numbers. Finally, chapter 11 gives some information on verification and validation of models; it also mentions literature sources and professional societies in the simulation area. This book is based on lecture notes that have been used for teaching simulation to students in management science, starting more than twen- ty years ago. The last few years this course has also been taught to students in Information Systems and they have indeed been able follow the statistical parts of this book. The simulation course is taught during 13 sessions of 90 minutes each; so the course takes 19.5 lec- ture hours in total. Besides this textbook there is a floppy disk with a collection of examples and exercises in Pascal, which can be ordered from the authors. These examples are used for demonstration purposes during the lectures. The exercises on the floppy disk are to be solved using some computer. These exercises closely follow the chapters of the book. After doing the exercises, the reader should be ready to start simulation projects in practice. This text is an adaptation of a book originally published in Dutch under the title "Simulatie: technieken en toepassingen", Academic Service, Schoonhoven, 1988. Tilburg, January 1991 J.P.C. Kleijnen W.J.H. van Groenendaal Post Box 90153 5000 LE Tilburg The Netherlands Electronic mail: kleijnen@kub.nl Phone: 013-662423 Fax: 013-663019 CONTENTS Preface Chapter 1 Introduction 1.1 Some Applications 1.2 Some Definitions References Chapter 2 Random numbers 2.1 Introduction 2.2 Methods for generating random numbers 2.3 Pseudorandom numbers 2.3.1 Midsquare method 2.3.2 Congruential method 2.3.3 Additive congruential method 2.3.4 Tausworthe generators 2.3.5 Shuffling and hyperplanes 2.4. Statistical tests for pseudorandom numbers 2.4.1 Frequency test 2.4.2 Test on pairs (r , r ) 2.4.3 Correlation test 2.4.4 Run tests 2.4.5 Gap test 2.4.6 Poker test 2.5 Practical computer usage References Chapter 3 Sampling from non-uniform distributions 3.1 "Table look up" method 3.1.1 Geometric distribution 3.2 Simulating the statistical process 3.2.1 Hypergeometric distribution 3.3 Inverse transformation method 3.3.1 Exponential distribution 3.3.2 Uniform distribution 3.4 Distributions related to the exponential distribution 3.4.1 Gamma, Erlang, chi-square and beta distributions 3.4.2 Poisson distribution 3.5 Normal distribution 3.5.1 Central limit theorem 3.5.2 Box-Muller method 3.6 Rejection method 3.6.1 Normal distribution 3.7 Distributions related to the normal distribution 3.7.1 Lognormal distribution 3.7.2 Chi square distribution 3.7.3 Student's t distribution 3.7.4 F distribution 3.8 Multivariate distributions 3.8.1 Multivariate normal distribution 3.9 Time series 3.9.1 Linear autocorrelation function 3.9.2 Exponential correlation function 3.9.3 Arbitrary correlation function 3.10 NAG routines References Chapter 4 Economic and corporate models 4.1 Macro-economic models 4.1.1 Klein's model 4.1.2 General macro-economic models: simultaneous equations 4.1.3 General macro-economic models: noise 4.2 Meso-economic models 4.2.1 Cobweb models 4.3 Micro-economic models 4.4 Micro-simulation 4.5 Corporate models 4.5.1 Example: Anheuser-Busch model 4.6 Risk analysis 4.7 System Dynamics 4.7.1 Mathematical formulation 4.7.2 Applications References Chapter 5 Operations Research models 5.1 Time 5.2 Inventory models 5.3 Queuing models 5.3.1 Single server 5.3.2 Parallel servers 5.3.3 Repairmen model 5.3.4 Servers in sequence Appendix: Analytic solution of M/M/n References Chapter 6 Simulation packages and languages 6.1 Simulation package for parallel servers 6.1.1 System components and organization 6.1.2 System states 6.1.3 Synchronization of server and transaction states 6.1.4 Data structures 6.2 Simulation languages References Chapter 7 Statistical applications 7.1 Regression analysis 7.2 Ordinary Least Squares (OLS) 7.3 Estimated Weighted Least Squares (EWLS) 7.4 Corrected Least Squares (CLS) 7.5 Monte Carlo study of EWLS and CLS 7.5.1 Verification of computer program 7.5.2 Asymptotic covariances in EWLS 7.5.3 Relative efficiency of EWLS and CLS 7.5.4 Confidence intervals in EWLS and CLS References Chapter 8 Regression metamodels 8.1 Metamodel concept 8.2 Regression data 8.3 Least squares 8.4 Validation of metamodels 8.4.1 Cross-validation 8.5 Detailed example of metamodeling: M/M/s 8.6 Interactions 8.6.1 FMS example References Chapter 9 Experimental Design 9.1. Classic experimental designs 9.1.1 One factor at a time 9.1.2 Full factorial designs 9.1.3 Incomplete factorial designs 9.2 Screening 9.3 Optimization: response surface methodology 9.3.1 Case study 9.4 Replications, and scaling effects References Chapter 10 Tactical aspects 10.1 Terminating models 10.2 Non-terminating, steady-state models 10.2.1 Nearly independent subruns: batching 10.2.2 Renewal analysis 10.3 Percentiles and quantiles 10.4 Variance reduction techniques 10.4.1 Common random numbers 10.4.2 Antithetics 10.5 Jackknifing References Chapter 11 Epilogue 11.1 Verification and validation 11.2 Further study References ------------------------------ Newsgroups: comp.simulation,comp.announce.conferences,comp.theory.dynamic-sys Path: saturn.sdsu.edu!rswiniar From: rswiniar%saturn@ucselx.sdsu.edu (Dr. Roman Swiniarski) Subject: Neural Networks Conference Originator: rswiniar@saturn.sdsu.edu Sender: news@ucselx.sdsu.edu (News Admin) Organization: San Diego State University, Math Dept. Date: Sun, 14 Apr 91 20:39:29 GMT Apparently-To: comp-simulation@ucsd.edu THE FIRST INTERNATIONAL CONFERENCE ON ``NEURAL NETWORKS IN SYSTEMS, CONTROL, VISION AND GENETICS'' (Organized by AMSE and San Diego State University) San Diego, 29-31 May, 1991 Howard Johnson Hotel, San Diego Down Town, 1430 Seventh Street, San Diego, CA 92101, tel (619) 6960911. Fax. (619) 2349416 SCIENTIFIC PROGRAM Communications are separated into 9 Groups, each corresponding to one Session: Methodology 1- Architectures 2- Learning Algorithms Applications 3- Signal and Data Processing 4- Patterns and Vision 5- Speech, Text and Image Recognition 6- System Analysis, Design and Inspections 7- Control and Robotics 8- Specific Applications Miscellaneous 9- Other Methods and Results REGISTRATION TUESDAY, May 28, 1991, 7:30-8:30 p.m. and WEDNESDAY May 29, 1991 8:30-9:15 a.m.: Reception of participants and registration. WEDNESDAY, May 29, 1991 Inaugural Session 9:15-10:30 Opening Addresses: Professor Guy Masnard, President of AMSE (France) - Opening Professor Donald Short, Dean of Faculty of Science SDSU - Inaugural Address Roman Swiniarski, SDSU - Chairman of the International Program Committee: Scientific Program of the Conference l 1- Inaugural Invited Lecture: 9:30-10:30 Professor Robert Hecht-Nielsen (University of California at San Diego, and HNC Neurocomputers, San Diego) ``Neural Computing Technology: A Status Report'' Coffee break 10:30-10:45 10:45-12:30 Plenary Session Invited Lectures: l 2- P. K. Simpson (General Dynamic Electronic Division, San Diego) ``Neural Networks and Fuzzy Systems'' l 3- Professor Peter Salamon (San Diego State University USA) "The ensemble oracle". Wednesday Afternoon Plenary Session 2:15 - 4:00 p.m. Invited Lectures l 4- Professor K. Lars Hansen (Electronic Institute The Technical University of Denmark (Denmark) "Self-repair in Neural Network Ensembles". l 5- Dr. Y. T. Zhou (HNC Neurocomputers, San Diego) Neural Methods for Computer Vision Problems. 4:00-4:15 Coffee break 4:15 p.m. Communications of Group 1: Neural networks Architectures THURSDAY, May 30, 1991 Two parallel Sessions: 8:30 a.m. Group 4 Group 7 Coffee break at 10:30 a.m. 2:00 p.m. Group 2 Group 9 Coffee break at 4:00 p.m. Dinner Party. FRIDAY, May 31, 1991 Two parallel Sessions: 8:30 a.m. Group 5 Group 8 Coffee break at 10:30 a.m. 2:00 p.m. Group 3 Group 6 Coffee break at 4:00 p.m. Conclusion of Conference. Closing Address. SESSIONS In each Session the papers will be presented according to their number given in the ``Detailed List of Accepted Communications'', unless otherwise specified by the Chairmen to have more homogeneous Sessions. The speakers are invited to contact the Chairmen at the beginning of the Session. The duration of the presentations will be decided by the Chairman according to the numbers of effectively presented papers, but 30 minutes, including the discussion, is the upper limit. Each Session will end with a General Panel Discussion about the topics of the Session: contact the Chairmen if you wish to take an active part in the discussion. DETAILED LIST OF ACCEPTED COMMUNICATIONS (This list does not include the additional late 40 papers that will be included soon.) Group 1 ARCHITECTURES 1. D. ALEXANDRIS, S. WOLPERT, M.T. MUSAVI (USA)- A VLSI implementations of a backpropagation neural network. 2. K. H. CHAN, M. T. MUSAVI (USA) - A probabilistic neural network classifier. 3. S. MONDUTIANI, D. MANDUTIANI (Romania)- Growing interconnection structure in neural network. 4. Z. WANG, C. DI MASSIMO (UK)- A procedure for determining the canonical structure of feedforward neural networks. 5. H. L. ZENG, J. B. YU (China)- A new method for energy functions and dynamics of continuous neural networks. 6. Y. H. SHI, Z. Y. HE (China)- A modified binary Hopfield neural network. 7. A. BULLER (Poland)- Neural screen: toward unified symbolic/connectionist knowledge processing. 8. C. NICOLOPOULOS (USA) - NEUGEN: A system for configuring neural network architectures. 9. S. HUI, S. ZAK (USA)- Stability analysis of cellular neural networks. 10. J. BUCKLEY, E. CZOGALA, T. SADOWSKI (USA and Poland)- Inversion of feedforward multilayer neural networks. Group 2 LEARNING ALGORITHMS 1. K. FARRIS, M. T., MUSAVI, W. AHMED (USA)- A clustering algorithm for implementation of RBF technique in neural networks. 2. M. ARNOLD, T. BAILEY, J. COWLES, J. CUPAL (USA)- Implementing back propagation neural nets with logarithmic arithmetic. 3. P. SARATCHANDRAN (Singapore)- Supervised learning via dynamic programming. 4. S. L. YANG, Y. KE, ZHONG WANG (China)- A modified Polak-Ribiere learning algorithm. 5. K. S. YI. W. Q. WU (China)- A new learning algorithm of neural networks-Adjoint network learning algorithm. 6. M. R. WALKER, L. A. AKERS (USA)- Information refinement in unsupervised, implementation-constrained neural neural networks. 7. Y. AUTRET, B. SOLAIMAN (France)- Defining sub-networks into the global neural network to increase the learning speed. 8. YANG X. H., PING HUE (China)- The effect of using variable learning rate in backpropagation (BP) algorithm. 9. SUN B. C., LIU X. H. (China)- A fast self-organizing neural network. 10. T. D. GEDEON, D. HARRIS (UK)- Network reduction techniques. 11. D. ZUCK (USA)- Defining theoretical consistent and therefore usable back-propagation neural net training data. 12. A. D. BACK, A.C. TSOI (Australia)- An adaptive lattice algorithm for IIR multilayer perceptron. 13. H. B. JI (China)- An investigation into the weighted learning scheme for autoassociative neural networks. 14. W. ABU-SALAMEH, M. R.TOLUN (Turkey)- Learning with extended backpropagation rule on different artificial neural network topologies. Group 3 SIGNALS AND DATA PROCESSING 1. F. L. LUO, ZHEN BAO (China)- Neural networks for recursive least squares algorithms. 2. F. L. LUO, ZHEN BAO (China)- Neural network approach to maximum likelihood. 3. Z. K. YANG, Q. . YIN, L. H. ZOU (China and USA)- Maximum likelihood direction estimating by neural network. 4. V. WUWONGSE (Thailand)- Neural network applied to identification of fuzzy membership function. 5. H. KRISHNAMURTHY, N. BALAKRISHNAN (India)- Neural network for aircraft identification and classification based on radar returns. 6. HWANG CHUNG, NING CHEN, J. H. PARK (USA)- Recognition of pulse signals mixed in the time domain. 7. TANG Y. Q., CAO J. H. YAO TANG (China)- Self-organizing neural map application. 8. Z. C. WANG, J. HANSON (Canada)- A neural M-ary tree method for data compression. 9. D. W. HU, Z. Z. WANG (China)- Study on multi-layered neural network approximating any continuous function. Group 4 PATTERNS AND VISION 1. P. L. HE, D. Q. LIANG, Y. L. CAI (China)- A neural network for windowed cepstrum filtering. 2. LUNG GUAN (Canada)- Comparing different neural network architectures for classifying X-ray imagery. 3. TAO WANG, X. L. XING (China)- Application of neural networks to image segmentation. 4. HUANG SI JUN, KOH SOO NGEE, TANG HUENG KEI (Singapore)- Comparing study of image data compression by neural networks and conventional transform method. 5. R. SWINIARSKI, M. BUTLER (USA)- Application of neural networks to image filtering. 6. Y. H. SHI, Z. Y. HE (China) - A combinator of two binary Hopfield neural network for associative memory. 7. T. V. NGUEN, Y. F. CHANG, C. M. CHOU (USA)- A tree search approach for pattern classification using Hopfield net. 8. L. THIAVILLE, P. PUGET, J. J. NIEZ (France)- A neural network for motion computation. 9. P. Y. BURGI, T. PUN (Switzerland)- Figure-group separation: Evidences for asynchronous processing in visual perception? 10. S. T. ACTON, W. N. KLARQUIST (USA)- Mean filed stereo correspondence. 11. M. B. ZAREMBA, E. PORADA (Canada) - Dipole neural processing: theory and applications. Group 5 SPEECH, TEXT a IMAGES RECOGNITION 1. E. C. MERTZANIS (UK)- Hierarchical volume data structure for position independent pattern recognition. 2. B. SALAIMAN, Y. AUTRET (France)- Handwritten numerals recognition using combined syntactic and neural network recognition methods. 3. Y. F. CHEN, Y, H. ZHOU (China)- An effective method of neural network for text recognition system. 4. T. D. GEDEON, V. MITAL (UK)- An adaptive learning network for information retrieval in a ligitation support application. 5. H. KABRE (France)- A latteral inhibition neural network for continuous speech recognition. 6. KIM KISEOK, HWANG HEEYEUNG (Korea)- A study on the speech recognition of Korean phonemes using modified recurrent perceptron model. 7. M. LEUNG, A. M. PETERSON (USA)- Multiple channel neural network model for texture analysis. 8. A. LUK, S. H. LEUNG (Hong Kong)- Effects of the numbers of hidden units on digit recognition.\vs\\ 9. T. V. NGUEN, C. M. CHOU, Y. F. CHANG (USA)- Multinetwork classifier for binary images. 10. M. Z. BLEYBERG, I. Y. ZAYAS, C. W. BLACK (USA)- A neural network system for grain classification. 11. M. B. ZAREMBA, W. J. BOCK, E. PORADA (Canada)- Recognition of measurement of optically detected physical variables using interactional neural network. 12. S. M. BHANDARI, K. V. V. MURTHY, S. S. S. P. RAO (India)- Artificial neural networks for character recognition. Group 6 SYSTEMS ANALYSIS, DESIGN and INSPECTION 1. J. G. XU, ZHONG WANG, YOUAN KE (China)- An adaptive feedback neural network for system modeling. 2. G. BOJADZIEV (Canada)- Zone stabilization of pattern formation by neural networks of chain type. 3. O. CITCILOGLU, E. TURCKAN, S. SEKER (Turkey and Netherlands)- Failure detection studies by layered neural network. 4. R. W. SWINIARSKI, T. ROGERS (USA)- Neural recurrent state estimator of dynamic systems: neural Kalman filtering. 5. M. H. MARZI, K. F. MARTIN (UK)- Artificial neural network in condition monitoring and fault diagnosis. 6. A. BULSARI, A. MEDVEDEV, H. SAXEN (Finland)- An algorithm for sensor fault detection using state vector estimator and feed-forward neural networks applied to a biochemical process. 7. D. R. SEIDL, R. D. LORENZ (USA)- A structure by which a recurrent neural network can approximate a nonlinear dynamic system. 8. Z. P. LO, B. BAVARIAN, C. R. TICC (USA)- The multiple traveling salesman problem formulation with artificial neural network and simulated annealing algorithms. 9. Z. P. LO, B. BAVARIAN (USA)- Development of an intelligent multi-sensor fusion (IMSF) system. Group 7 CONTROL AND ROBOTICS 1. D. W. HU, Z. Z. WANG (China)- Robot eye/hand system coordinated by neural network. 2. D. MANDUTIANU (Romania)- A neural approach for mobile robot control. 3. R. W. SWINIARSKI (USA)- Neural network application to self-tuning PID control based on relay feedback and pattern recognition approach. 4. NING CHEN, HWANG CHUNG (USA)- A neural network based real time robot path planner in known environment. 5. G. E. MOBUS, P. S. FISHER (USA)- Conditioned response training of robot using Adaptrode-based neural networks: I-Continuous adaptive learning. 6. G. E. MOBUS, P. S. FISHER (USA)- Conditioned response training of robot using Adaptrode-based neural networks: II- Simulation results. 7. D. Q.CHEN, F. Q.ZHOU (China)- A neurally-based controller for target-tracking sight control system. 8. YANG JUN, ZHOU F. Q. (China)- The variable structure adaptive control using neural networks. 9. B. APOLONI (Italy)- A cooperative neural network endowed architecture for model reference adaptive control. 10. J. G. FU, N. SINHA (Canada)- Iterative learning control of robotics manipulators with neural networks. 11. LEEI GER, HSUNG CHEN (Canada)- Direct neural network controller for dynamic nonlinear system. Group 8 SPECIFIC APPLICATIONS 1. A. KOS (Poland)- The multi-layer perceptron as a toll for hybrid circuits topology design. 2. P. KOROHODA, A. KOS (Poland)- A neural network for power circuits topology design. 3. F. PANETSOS, J. M. ZALDIVAR (Italy)- Batch chemical reactors control using neural networks. 4. J. H. GE, Y. X. SUN (China)- Identification of static model of chemical process based on neural network. 5. R. W. SWINIARSKI, G. OCHS, D. WAAGEN (USA)- Application of neural networks to human genome sequencing. 6. P. BOURRET, C. GASPIN (France)- Prediction of RNAs secondary structures by Boltzman machine. 7. O. V. BORISOVA, J. I. PETUNIN (USSRR)- Mathematical modeling of synaptic transmission. 8. N. E. SONDAK, V. K. SONDAK (USA)- A structured methodology for the construction of artificial neural network 9. M. MANDAL, A. K. MANDAL, A. K. NATH (India)- Neural network models for the estimation of myocardial infraction size. 10. V. VENUGOPAL, T. T. MERENDRAN (India)- Neural network approach for designing cellular manufacturing systems. 11. H. BOHR, R. GREENSTEIN, P. WOLYNES (USA)- Neural network predicting surface structure of protein from their sequence. Group 9 OTHER METHODS AND RESULTS 1. M. RAMADAN, M. I. EL-SINGABY, N. N. SORIAL (Egypt)- Zakian's error criteria with sensitivity constraint. 2. C. KRAWCZYK (Poland)- Several variable interior functions. 3. T. GUNEL, B. YAZGAN (Turkey)- A new optimization method based on simulated annealing algorithm. 4. ZHA MING, LUO SIWEI, GE N. K. (China)- A method of parallel processing for linear prediction of speech. 5. M. B. ELABRI, M. DUFAUT, R. HUSSON (France)- Map database construction and scene prediction for visual navigation. 6. J. FACON (Brazil)- Translation clustering method based on optical flow. 7. J. L. FENG, QUIN ZHOU (China)- Visualization of image texture information. 8. T. KRISHNAN (India)- Imperfect supervision in statistical pattern recognition. 9. R. L. LIN, S. J. XU, F. M. SARGOS (France)- Stability robustness analysis of dynamic interval systems. 10. K. KOLOWROCKI (Poland)- Examples of asymptotics for nonhomogeneous parallel-series systems. 11. M. B. ELABRI, M. DUFAUT, R. HUSSON (France)- Road edge detection for mobile robot navigation. Study of the initial phase. 12. H. Al-KAID, H. C. BAR, K. AXTZRWEWITZY (Spain)- On the design of asymptotic continuous-time output proportional regulators with bang-bang gains. 13. G. BOJADZIEV, M. SAIF (Canada)- Control of planar manipulators with uncertain masses. 14. J. ZAREBSKI (Poland)- Analysis of electrical and thermal transients in electronic circuits. 15. YI H. J., ZENG R. S. (China)- An optimal scheduling problem in a bus line. 16. B. L. KUCHIN, L. A. OVCHAROV (USSR)- The optimal control of distribution systems with fuzzy initial information. ACCOMODATION We have chosen to hold the Conference in a good and cheap Hotel. We highly recommend to have also your bedroom in this Hotel. The rate for the participants is US $ 49 per night for single or double room, including breakfast (including the taxes, you to have pay US $53.41). AMSE can book the room for you: you must send to AMSE a request with whole amount included (and we must receive request before May 1, 1991). If not, you may contact and pay the Hotel by yourself: Howard Johnson Hotel (San Diego Down Town), 1430 Seventh Ave, San Diego, CA 92101, Fax 619-2349416, tel. 619-6960911 (indicate the Reference "AMSE Conference", John Tidwell). CONFERENCE FEE The Conference fee will include: Volume of the Summaries of all accepted papers, the Volumes of the Conference Proceedings including all the presented papers, possible publication of the papers in original or revised form in a special Monograph of the AMSE Series ``Neural Networks'' with a low subscription rate, the admission to the Dinner Party on Thursday Evening. Payment arrived before May 1, 1991: US $ 285 Payment arriving from May 1, 1991 or at the Conference Desk: US $ 320 Discounts: There is a discount of US $ 30 for students and if several participants of the same Department register together. Accompanying students or co-authors pay reduced fee of US $ 80 (without the Volumes). Make payments in advance to the order of AMSE, make preferably a Bank Transfer to'Discount bank and Trust Company, Geneve, Switzerland, to credit 64373. Participant working in France will pay in equivalent French Francs: credit the AMSE Postal account 'Lyon, 2708-62 N'. ``Unesco Coupons'' sent to AMSE Office are also accepted. The registration is possible during the Conference, but, for better organization, we wish that all attendies mail in advance to AMSE, 16 Avenue de Grange Blanche, 69160 Tassin-la-Demi-Lune, France, Telex: 389000 (you must put at the beginning of the text the AMSE code: A74595-AMSEMESNARD+), Tel. 33-78343604, Fax: 33-78345417 Chair of the International Program Committee Roman Swiniarski Department of Mathematical Sciences San Diego State University San Diego , CA 92182-0314, USA e-mail: rswiniar@ania.sdsu.edu Tel: (619) 594-5538 Fax: 619-594-5642 PS. The conference title has been changed to ``Neural Networks: Mehodologies and Applications''. ------------------------------ END OF SIMULATION DIGEST ************************