Simulation Experiments for Neural Network Learning,

Abstract

This paper investigates approaches to the design of simulation experiments for training neural networks which are to be used as classifiers. Hierarchical clustering applied to the ART1 and ART2 (ART = Adaptive Resonance Theory) neural network architectures developed by Carpenter and Grossberg 20,21) is the basis for the approach. A series of experiments based on this approach will test the performance of ART1 and ART2 as pattern classifiers against a variety of real and artificial data sets. The issues to be investigated in these experiments include the sensitivity of performance to a variety of network parameters, pattern characteristics, and pattern presentation disciplines. A background is provided for those unfamiliar with neural networks in general, and with Grossberg's approach in particular.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007151

Entities

People

  • David S. Newman

Organizations

  • Boeing

Tags

DTIC Thesaurus Topics

  • Computer Science
  • Computing System Architectures
  • Data Science
  • Data Sets
  • Information Science
  • Machine Learning
  • Network Architecture
  • Network Science
  • Neural Networks
  • Simulations
  • Theoretical Computer Science

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks