Knowledge Processing system using Chaotic Associative Memory

Yuko Osana, Masafumi Hagiwara

研究成果: Paper査読

抄録

In this paper, we propose a Knowledge Processing system using Chaotic Associative Memory (KPCAM). The proposed KPCAM 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 and to deal with many-to-many associations. In this research, the CAM is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: (1) it can deal with the knowledge which is represented in a form of semantic network; (2) it can deal with characteristics inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.

本文言語English
ページ746-751
ページ数6
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

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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