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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:


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
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


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