Using 3-D Surface Maps to Illustrate Neural Network Performance

Abstract

This paper suggests a possible means for evaluating the performance of neural networks from a global perspective in parameter-space. Traditional evaluations tend to focus on performance in weight-space or on overall output error during one training session. However, a global perspective of performance in parameter-space may be of primary importance during the initial stages of problem solution. During these stages, the researcher is typically trying to determine a network configuration and suitable values for its training equation parameters. Instead of a hit-or-miss approach, this paper describes an organized experimental method that identifies network configuration and parameter value choices which are not sensitive to minor variations for a standard training metric. The technique is illustrated for the network used by Hopfield and Tank to solve a traveling salesman problem and with traditional Backpropagation as described by Lippmann.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA578295

Entities

People

  • Peter G. Raeth

Organizations

  • Wright Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computing System Architectures
  • Convergence
  • Electronics
  • Equations
  • Gain
  • Information Operations
  • Learning
  • Mesh Networks
  • Military Applications
  • Momentum
  • Network Architecture
  • Neural Networks
  • Simulations
  • Standards
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space