Chaotic analog associative memory

Hisao Imai, Yuko Osana, Masafumi Hagiwara

研究成果: Article査読

4 被引用数 (Scopus)


This paper proposes chaotic analog associative memory (CAAM), which can handle one-to-many learning pairs composed of analog patterns. Most past associative memory models have considered a binary pattern as the pattern to be learned, which makes it difficult to handle analog patterns. It is also a problem that the superposed pattern is recalled due to interaction between the memorized patterns. Thus, it becomes difficult to handle the association of one-to-many learning pairs, in which multiple patterns can be recalled from a single given pattern. In contrast, the proposed model uses a multiwinner self-organizing neural network (MWSONN), which can handle analog patterns, and realizes association of the analog patterns. In the proposed CAAM, the chaotic neuron is introduced as a part of the network, and one-to-many association is realized by utilizing the dynamic recall power of the chaotic neuron. Computer experiments verify that the association of one-to-many learning pairs composed of analog patterns can be realized, and that the proposed model has high noise immunity and robustness to faults.

ジャーナルSystems and Computers in Japan
出版ステータスPublished - 2005 4月 1

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 情報システム
  • ハードウェアとアーキテクチャ
  • 計算理論と計算数学


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