Gray-level discrete associative memory

Francis T.S. Yu, Chii Maw Uang, Shizhuo Yin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A gray-level discrete associative-memory neural network based on object decomposition and composition is presented. By decomposing a gray-level pattern into bipolar/binary subpatterns, a gray-level discrete associative memory can be constructed from the composition of the subpattern channel results. Preprocessing for removing dc bias and normalizing the gray-level scale is performed on the input gray-level pattern. This eliminates the mismatching and saturation problems caused by bias level, which shifts the pattern gray levels throughout the pattern. Computer-simulation and optical-experimental results for a gray-level interpattern association model are shown to be consistent with the theoretical model.

Original languageEnglish (US)
Pages (from-to)1322-1329
Number of pages8
JournalApplied Optics
Volume32
Issue number8
DOIs
StatePublished - Mar 10 1993

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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