An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization

Ryoma Bise, Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.

Original languageEnglish
Pages (from-to)1569-1574
Number of pages6
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume50
Issue number12
DOIs
Publication statusPublished - Dec 2003
Externally publishedYes

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Cellular neural networks
Data storage equipment
Computer simulation

Keywords

  • Associative memory
  • Basin of attraction
  • Cellular neural networks (CNNs)
  • Generalized eigenvalue minimization (GEVM)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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AU - Bise, Ryoma

AU - Takahashi, Norikazu

AU - Nishi, Tetsuo

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N2 - Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.

AB - Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.

KW - Associative memory

KW - Basin of attraction

KW - Cellular neural networks (CNNs)

KW - Generalized eigenvalue minimization (GEVM)

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