A new methodology to the design of associative memories based on cellular neural networks

Hye Yeon Kim, Jooyoung Park, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

In this paper, we consider the problem of realizing associative memories via cellular neural networks(CNNs). We formulate the synthesis of CNN that can store given binary vectors with improved performance as a constrained optimization problem. Next, we convert the synthesis problem into a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by computer simulations.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages965-968
Number of pages4
Volume15
Edition2
Publication statusPublished - 2000

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Kim, H. Y., Park, J., & Lee, S. W. (2000). A new methodology to the design of associative memories based on cellular neural networks. In Proceedings - International Conference on Pattern Recognition (2 ed., Vol. 15, pp. 965-968)