On the memorization accuracy of autoassociative memory models

Kazuaki Masuda, Eitaro Aiyoshi

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

3 Citations (Scopus)

Abstract

An autoassociative memory which is modeled as the standard recurrent neural network (N.N.) is capable of storing multiple patterns and subsequently recalling one of them in response to an input signal. However, we found in our recent trials that it can't always recall correct patterns accurately. In this paper, we demonstrate such phenomena by numerical examples and identify the cause of memorization errors. We also propose an immediate solution to memorize correct patterns without fail by storing extra patterns at the same time.

Original languageEnglish
Title of host publicationSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
PublisherSociety of Instrument and Control Engineers (SICE)
Pages530-536
Number of pages7
ISBN (Print)9784907764395
Publication statusPublished - 2011 Jan 1
Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
Duration: 2011 Sep 132011 Sep 18

Publication series

NameProceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
CountryJapan
CityTokyo
Period11/9/1311/9/18

Keywords

  • autoassociative memory
  • local optimality
  • nonlinear dynamical system
  • recurrent neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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