On the memorization accuracy of autoassociative memory models

Kazuaki Masuda, Eitaro Aiyoshi

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
出版社Society of Instrument and Control Engineers (SICE)
ページ530-536
ページ数7
ISBN(印刷版)9784907764395
出版ステータスPublished - 2011 1 1
イベント50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
継続期間: 2011 9 132011 9 18

出版物シリーズ

名前Proceedings 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

ASJC Scopus subject areas

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

フィンガープリント 「On the memorization accuracy of autoassociative memory models」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル