Chaotic Associative Memory for Sequential Patterns

Yuko Osana, Masafumi Hagiwara

研究成果: Paper査読

1 被引用数 (Scopus)


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.

出版ステータスPublished - 1999
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 1999 7月 101999 7月 16


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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