VLSI Implementation of Neuromorphic Learning Networks

Abstract

The researchers have shown how to rigorously derive deterministic systems from stochastic ones in the Boltzmann machine framework that they are using for their implementations. They have further shown how to search for new learning algorithms suitable for VLSI implementation using a genetic algorithm approach. They-have analyzed the effect of precision constraints such as is found in hardware implementations on the learning and generalization abilities of neural networks. They have studied the learning behavior of neural networks under conditions where they where they can or cannot classify perfectly.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 1993
Accession Number
ADA270825

Entities

People

  • Joshua Alspector

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computational Science
  • Generators
  • Genetic Algorithms
  • Information Processing
  • Information Science
  • Information Systems
  • Integrated Circuits
  • Learning
  • Machine Learning
  • Network Science
  • Neural Networks
  • Precision
  • Signal Processing
  • Simulations

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

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