Optical Computing Based on the Hopfield Model for Neural Networks

Abstract

Associative memories are one of the most interesting applications of neural networks. In general, an associative memory stores a set of information, called memories. The information is stored in a format such that when an external stimulus is presented into the system, the system evolves to a stable state that is closest to the input data. We can view this process as a content- addressable memory since the stored memory is retrieved by the contents of the input and not by the specific address. In other words, the memory can recognize distorted inputs as long as the input provides sufficient information. Later in this report we will show the characteristics of the associative memory by presenting distorted versions of the stored images, e.g., rotated, scaled, shifted ones, etc. to the system and see how it converges.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA211824

Entities

People

  • Demetri Psaltis
  • Hsin-yu Li
  • Ken-yuh Hsu

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Computer Simulations
  • Computers
  • Content Addressable Memory
  • Correlators
  • Cross Correlation
  • Distortion
  • Equations
  • Frequency
  • High Gain
  • Modulation
  • Neural Networks
  • Recognition
  • Recurrent Neural Networks
  • Simulations
  • Two Dimensional
  • Vector Spaces

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

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