PDP & NN5

[TEPPER@CVAX.IPFW.INDIANA.EDU]

        Fifth NN & PDP CONFERENCE PROGRAM - April 9, 10 and 11,1992
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The Fifth Conference on Neural Networks and Parallel Distributed Processing
at Indiana University-Purdue University at Fort Wayne will be held April 9,
10, and 11, 1992.  Conference registration is $20 (on site).  Students and
members or employees of supporting organizations attend free.  Some limited
financial support might also be available to allow students to attend.  

Inquiries should be addressed to:  

US mail:  
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Pr. Samir Sayegh                                               
Physics Department   
Indiana University-Purdue University
Fort Wayne, IN 46805-1499

email:  sayegh at ipfwcvax.bitnet
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FAX:    (219)481-6880
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Voice:  (219) 481-6306 OR 481-6157
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All talks will be held in Kettler Hall, Room G46:
Thursday, April 9, 6pm-9pm; Friday Morning & Afternoon (Tutorial Sessions),
8:30am-12pm & 1pm-4:30pm and Friday Evening 6pm-9pm; Saturday, 9am-12noon.

Parking will be available near the Athletic Building or at any Blue A-B
parking lots.  Do not park in an Orange A lot or you may get a parking
violation ticket.

Special hotel rates (IPFW corporate rates) are available at Canterbury Green,
which is a 5 minute drive from the campus.  The number is (219) 485-9619.
The Marriott Hotel also has corporate rates for IPFW and is about a 10 minute
drive. Their number is (219) 484-0411.
Another hotel with corporate rates for IPFW is Don Hall's Guesthouse (about 10
minutes away).  Their number is (219) 489-2524.

The following talks will be presented:

Applications I  -  Thursday 6pm-7:30pm
--------------------------------------

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

J.L. Fleming & D.G. Hill, Armstrong Lab, Brooks AFB.  STUDENT MODELING USING
           ARTIFICIAL NEURAL NETWORKS


Biological and Cooperative Phenomena Optimization I  -  Thursday 7:50pm-9pm
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Ljubomir T. Citkusev & Ljubomir J.,  Buturovic, Boston University.  NON-
           DERIVATIVE NETWORK FOR EARLY VISION

Yalin Hu & Robert J. Jannarone, University of South Carolina.  A
           NEUROCOMPUTING KERNEL ALGORITHM FOR REAL-TIME, CONTINUOUS COGNITIVE
           PROCESSING 

M.B. Khatri & P.G. Madhavan, Indiana-Purdue University, Indianapolis.  ANN
           SIMULATION OF THE PLACE CELL PHENOMENON USING CUE SIZE RATIO

Mark M. Millonas, University of Texas at Austin.  CONNECTIONISM AND SWARM
           INTELLIGENCE

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Tutorials I  -  Friday 8:30am-11:45am 
-------------------------------------

Bill Frederick, Indiana-Purdue University.  INTRODUCTION TO FUZZY LOGIC

Helmut Heller, University of Illinois.  INTRODUCTION TO TRANSPUTER SYSTEMS

Arun Jagota, SUNY-Buffalo.  THE HOPFIELD NETWORK, ASSOCIATIVE MEMORIES, AND
           OPTIMIZATION


Tutorials II  -  Friday 1:15pm-4:30pm 
-------------------------------------

Krzysztof J. Cios, University Of Toledo.  SELF-GENERATING NEURAL NETWORK
           ALGORITHM : CID3 APPLICATION TO CARDIOLOGY 

Robert J. Jannarone, University of South Carolina.  REAL-TIME NEUROCOMPUTING,
           AN INTRODUCTION 


Network Analysis I  -  Friday 6pm-7:30pm
----------------------------------------

M.R. Banan & K.D. Hjelmstad, University of Illinois at Urbana-Champaign.  A
           SUPERVISED TRAINING ENVIRONMENT BASED ON LOCAL ADAPTATION, FUZZINESS,
           AND SIMULATION

Pranab K. Das II, University of Texas at Austin.  CHAOS IN A SYSTEM OF FEW
           NEURONS

Arun Maskara & Andrew Noetzel, University Heights.  FORCED LEARNING IN SIMPLE
           RECURRENT NEURAL NETWORKS

Samir I. Sayegh, Indiana-Purdue University.  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


Applications II  -  Friday 7:50pm-9pm
-------------------------------------

Susith Fernando & Karan Watson, Texas A & M University.  ANNs TO INCORPORATE
           ENVIRONMENTAL FACTORS IN HI FAULTS  DETECTION

D.K. Singh, G.V. Kudav, & T.T. Maxwell, Youngstown State University. 
           FUNCTIONAL MAPPING OF SURFACE PRESSURES ON 2-D AUTOMOTIVE SHAPES BY
           NEURAL NETWORKS

K. 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 NETWOR

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Network Analysis II  -  Saturday 9am-10:30am
-------------------------------------------

Sandip Sen, University of Michigan.  NOISE SENSITIVITY IN A SIMPLE CLASSIFIER
           SYSTEM

Xin Wang, University of Southern California.  DYNAMICS OF DISCRETE-TIME
           RECURRENT NEURAL NETWORKS: PATTERN FORMATION AND EVOLUTION

Zhenni Wang and Christine Di Massimo, University of Newcastle. A PROCEDURE
           FOR DETERMINING THE CANONICAL STRUCTURE OF MULTILAYER NEURAL NETWORKS

Srikanth Radhakrishnan, Tulane University.  PATTERN CLASSIFICATION USING THE
HYBRID COULOMB ENERGY NETWORK


Biological and Cooperative Phenomena Optimization II  - Saturday 10:50am-12noon 
-------------------------------------------------------------------------------

J. Wu, M. Penna, P.G. Madhavan, & L. Zheng, Purdue University at
           Indianapolis.  COGNITIVE MAP BUILDING AND NAVIGATION

C. Zhu, J. Wu, & Michael A. Penna, Purdue University at Indianapolis.  USING
           THE NADEL TO SOLVE THE CORRESPONDENCE PROBLEM

Arun Jagota, SUNY-Buffalo.   COMPUTATIONAL COMPLEXITY OF ANALYZING A
           HOPFIELD-CLIQUE NETWORK

Assaad Makki, & Pepe Siy, Wayne State University.  OPTIMAL SOLUTIONS BY
MODIFIED HOPFIELD NEURAL NETWORKS