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

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