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Pulin
11-29-1993, 03:47 AM
The Proceedings of the Fifth Conference on Neural Networks and Parallel
Distributed Processing at Indiana University-Purdue University at Fort Wayne,
held April 9-11, 1992 are now available. They can be ordered ($9 + $1 U.S.
mail cost; make checks payable to IPFW) from:

Secretary, Department of Physics FAX: (219)481-6880
Voice: (219)481-6306 OR 481-6157
Indiana University Purdue University Fort Wayne
email: proceedings@ipfwcvax.bitnet
Fort Wayne, IN 46805-1499

The following papers are included in the Proceedings of the Fifth Conference:

Tutorials

Phil Best, Miami University, Processing of Spatial Information in the Brain
William Frederick, Indiana-Purdue University, Introduction to Fuzzy Logic
Helmut Heller and K. Schulten, University of Illinois, Parallel Distributed
Computing for Molecular Dynamics: Simulation of Large Hetrogenous
Systems on a Systolic Ring of Transputer
Krzysztof J. Cios, University Of Toledo, An Algorithm Which Self-Generates
Neural Network Architecture - Summary of Tutorial

Biological and Cooperative Phenomena Optimization

Ljubomir T. Citkusev & Ljubomir J. Buturovic, Boston University, Non-
Derivative Network for Early Vision
M.B. Khatri & P.G. Madhavan, Indiana-Purdue University, Indianapolis, ANN
Simulation of the Place Cell Phenomenon Using Cue Size Ratio
J. Wu, M. Penna, P.G. Madhavan, & L. Zheng, Purdue University at
Indianapolis, Cognitive Map Building and Navigation
J. Wu, C. Zhu, Michael A. Penna & S. Ochs, Purdue University at
Indianapolis, Using the NADEL to Solve the Correspondence Problem
Arun Jagota, SUNY-Buffalo, On the Computational Complexity of Analyzing
a Hopfield-Clique Network

Network Analysis

M.R. Banan & K.D. Hjelmstad, University of Illinois at Urbana-Champaign,
A Supervised Training Environment Based on Local Adaptation,
Fuzzyness, and Simulation
Pranab K. Das II & W.C. Schieve, University of Texas at Austin, Memory in
Small Hopfield Neural Networks: Fixed Points, Limit Cycles and Chaos
Arun Maskara & Andrew Noetzel, Polytechnic University, Forced Learning in
Simple Recurrent Neural Networks
Samir I. Sayegh, Indiana-Purdue University, Neural Networks Sequential vs
Cumulative Update: An * Expansion
D.A. Brown, P.L.N. Murthy, & L. Berke, The College of Wooster, Self-
Adaptation in Backpropagation Networks Through Variable
Decomposition and Output Set Decomposition
Sandip Sen, University of Michigan, Noise Sensitivity in a Simple Classifier
System
Xin Wang, University of Southern California, Complex Dynamics of Discrete-
Time Neural Networks
Zhenni Wang and Christine di Massimo, University of Newcastle, A Procedure
for Determining the Canonical Structure of Multilayer Feedforward
Neural Networks
Srikanth Radhakrishnan and C, Koutsougeras, Tulane University, Pattern
Classification Using the Hybrid Coulomb Energy Network

Applications

K.D. Hooks, A. Malkani, & L. C. Rabelo, Ohio University, Application of
Artificial Neural Networks in Quality Control Charts
B.E. Stephens & P.G. Madhavan, Purdue University at Indianapolis, Simple
Nonlinear Curve Fitting Using the Artificial Neural Network
Nasser Ansari & Janusz A. Starzyk, Ohio University, Distance Field Approach
to Handwritten Character Recognition
Thomas L. Hemminger & Yoh-Han Pao, Case Western Reserve University, A
Real-Time Neural-Net Computing Approach to the Detection and
Classification of Underwater Acoustic Transients
Seibert L. Murphy & Samir I. Sayegh, Indiana-Purdue University, Analysis of
the Classification Performance of a Back Propagation Neural Network
Designed for Acoustic Screening
S. Keyvan, L. C. Rabelo, & A. Malkani, Ohio University, Nuclear Diagnostic
Monitoring System Using Adaptive Resonance Theory