Chaotic Associative Memory for Sequential Patterns

Yuko Osana, Masafumi Hagiwara

研究成果: Paper

1 引用 (Scopus)

抜粋

In this paper, we propose a Chaotic Associative Memory for Sequential Patterns (CAMSP). The proposed CAMSP is based on a Chaotic Associative Memory (CAM) composed of chaotic neurons. In the conventional chaotic neural network, when a stored pattern is given to the network as an external input continuously, around the input pattern is searched. The CAM makes use of this property in order to separate superimposed patterns. In this research, the CAM is applied to associations for sequential patterns. The proposed model has the following features: (1) it can deal with associations for the sequential patterns; (2) it can realize associations considering patterns' history; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed model.

元の言語English
ページ752-757
ページ数6
出版物ステータスPublished - 1999 12 1
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 1999 7 101999 7 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
Washington, DC, USA
期間99/7/1099/7/16

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

フィンガープリント Chaotic Associative Memory for Sequential Patterns' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Osana, Y., & Hagiwara, M. (1999). Chaotic Associative Memory for Sequential Patterns. 752-757. 論文発表場所 International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .