On the design method of cellular neural networks for associative memories based on generalized eigenvalue problem

R. Bise, N. Takahashi, T. Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a design technique which is used to realize associative memories via cellular neural networks. The proposed method can store every prototype vector as a memory vector and maximize the areas of basin of attraction of memory vectors in a certain sense. The network parameters are obtained by solving optimization problems known as generalized eigenvalue problems. Simulation results prove that our method is better than the existing ones.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
EditorsRonald Tetzlaff
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages515-522
Number of pages8
ISBN (Electronic)981238121X
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002 - Frankfurt, Germany
Duration: Jul 22 2002Jul 24 2002

Publication series

NameProceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications
Volume2002-January

Other

Other7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
Country/TerritoryGermany
CityFrankfurt
Period7/22/027/24/02

Keywords

  • Associative memory
  • Cellular neural networks
  • Computer simulation
  • Design methodology
  • Differential equations
  • Eigenvalues and eigenfunctions
  • Electronic mail
  • Neural networks
  • Prototypes
  • Vectors

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering
  • Modelling and Simulation

Fingerprint

Dive into the research topics of 'On the design method of cellular neural networks for associative memories based on generalized eigenvalue problem'. Together they form a unique fingerprint.

Cite this