Training Neural Networks with Weight Constraints

Abstract

Hardware implementation of artificial neural networks imposes a variety of constraints. Finite weight magnitudes exist in both digital and analog devices. Additional limitations are encountered due to the imprecise nature of hardware components. These constraints can be overcome with a stochastic global optimization strategy which effectively searches the range of the weight space and is robust to quantization and modeling errors. Evolutionary programming is proposed as a solution to training networks with these constraints. This work investigates the use of evolutionary programming in optimizing a network with weight constraints. Comparisons are made to the backpropagation training algorithm for networks with both unconstrained and hard-limited weight magnitudes. Neural networks, Analog, Digital, Stochastic

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1993
Accession Number
ADA264665

Entities

People

  • John R. Mcdonnell

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Computer Science
  • Artificial Intelligence
  • Cognition
  • Computer Programming
  • Computer Science
  • Computers
  • Demographic Cohorts
  • Genetic Algorithms
  • Learning
  • Microstructure
  • Neural Networks
  • Ocean Surveillance
  • Optimization
  • Personal Information Managers
  • Signal Processing
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

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