Optical Computing Research

Abstract

The research focused on understanding the global as well as local properties of the neural network model. Global properties are the dynamics of the network, convergency properties, computational power and capacity. Local properties mean the theory of threshold logic elements, the basic building blocks of the network. Investigated is the relation between error-correcting codes and neural networks. The motivation was that a neural network model can be viewed as a decoder. The stable states correspond to codewords, the probe vector corresponds to the received vector, and convergence to the closest stable state corresponds to Maximum Likelihood Decoding (MLD). Several natural ways were found for connecting the concepts of error correcting codes with the concept of neural networks. The MLD problem in a linear block code is equivalent to finding the global maximum of the energy function of a neural network that can be easily constructed knowing the basis set of the code.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1988
Accession Number
ADA202963

Entities

People

  • Jehosua Bruck
  • Joseph W. Goodman

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Coding
  • Computations
  • Computer Programming
  • Computer Science
  • Computers
  • Decoding
  • Information Processing
  • Information Systems
  • Logic
  • Logic Elements
  • Neural Networks
  • Notation
  • Symbols
  • Time Intervals
  • Vector Spaces

Readers

  • Computer Programming and Software Development.
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

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