Photonic Technology Development for Densely Interconnected Neural Networks: Augmentation Award.

Abstract

Technical accomplishments under the grant, 'Photonic Technology Development for Densely Interconnected Neural Networks: Augmentation Award' (AFOSR Grant No. F49620-93-1-0445), B. K. Jenkins, P.I. are described. They include an analysis of convergence conditions and properties of backward error propagation learning in photorefractive based optical neural networks. The analysis includes implementations based on fully coherent single source architectures and on incoherent/coherent multiple source architectures. Also analyzed in terms of their effect on optical neural network learning are spatial light modulator limitations such as finite modulator contrast ratio, detector noise, and limited detector dynamic range. Additionally, we have developed a technique for compensating for photorefractive grating decay during neural network learning, by varying two parameters, spatial light modulator gain and photorefractive crystal exposure energy, according to a prescribed schedule.

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Document Details

Document Type
Technical Report
Publication Date
Jan 24, 1997
Accession Number
ADA320927

Entities

People

  • B. K. Jenkins

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • California
  • Computing System Architectures
  • Contrast
  • Convergence
  • Detectors
  • Dynamic Range
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Learning
  • Modulators
  • Network Architecture
  • Neural Networks
  • Optical Modulators

Fields of Study

  • Physics

Readers

  • Artificial Intelligence
  • Radar Systems Engineering.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks