Character Recognition Using Novel Optoelectronic Neural Network

Abstract

A novel optoelectronic neural network has been designed and constructed to recognize a set of characters from the alphabet. The network consists of a 15 x 1 binary input vector, two optoelectronic vector matrix multiplication layers, and a 15 X 1 binary output layer. The network utilizes a pair of custom fabricated Spatial Light Modulators (SLMs) with 120 levels of gray scale per pixel. The SLMs realize the matrix weights. Previous networks of this type were hampered by limited levels of gray scale and the need to use two separate weight masks (matrices) per layer. The weight masks are operated in unipolar mode. This allows both positive and negative weights to be realized from the same mask. A hard limiting function is used for the network's nonlinearity. A modification of Widrow's lesser known MR2 training algorithm is used to train the network. Furthermore, the network introduces a novel lens-free crossbar matrix-vector multiplier.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1993
Accession Number
ADA283339

Entities

People

  • William M. Robinson

Organizations

  • University of Texas at San Antonio

Tags

Communities of Interest

  • Advanced Electronics
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Character Recognition
  • Circuit Boards
  • Clocks
  • Computer Programming
  • Computer Programs
  • Computers
  • Detectors
  • Electrical Engineering
  • Gray Scale
  • Modulators
  • Neural Networks
  • Optical Modulators
  • Self Organizing Systems
  • Semiconductors
  • Training

Readers

  • Computer Programming and Software Development.
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems