Practical Issues in the Complexity of Neural Networks

Abstract

The equipment purchased under this Grant was used to supplement the theoretical work done under AFOSR-87-0400 with experimental results. The primary use of the equipment was to perform experiments to aid in the generation and testing of theoretical hypotheses about neural networks, regarding the magnitude of synaptic weights, convergence of learning algorithms, computation and learning with bounded-precision analog neural networks, the performance of simulated annealing on structured problems, and the management of replicated data bases. Research is still underway to gather more experimental data and provide theoretical justification for the observations. Keywords: Neural networks, Complexity theory, Fault tolerance, Learning.

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Document Details

Document Type
Technical Report
Publication Date
Jan 29, 1990
Accession Number
ADA221420

Entities

People

  • Georg Schnitger
  • Ian Parberry
  • Piotr Berman

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Annealing
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computational Complexity
  • Computations
  • Computer Graphics
  • Computer Science
  • Computers
  • Convergence
  • Databases
  • Experimental Data
  • Information Systems
  • Learning
  • Neural Networks
  • Precision

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neuroscience
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks