Network Reduction Using Error Prediction

Abstract

This thesis investigates gradient descent learning algorithms for multi-layer feed forward neural networks. A technique is developed which uses error prediction to reduce the number of weights/nodes in a network. The research begins by studying the first and second order back-prop training algorithms along with their convergence properties. A network is reduced by making an estimate of the amount of error which would occur when a weight(s) is removed. This error estimate is then used to determine if a particular weight is essential to the operation of the network. If not, it is removed and the network retrained. The process is repeated until the network is reduced to the desired size, or the error becomes unacceptable.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA230755

Entities

People

  • Michael V. Gilsdorf

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Automated Speech Recognition
  • Classification
  • Computational Complexity
  • Convergence
  • Learning
  • Literature Surveys
  • Mathematical Analysis
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Security
  • Standards
  • Theses
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computer Networking

Technology Areas

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