Knowledge Processing system using Improved Chaotic Associative Memory

Yuko Osana, Masafumi Hagiwara

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

In this paper, we propose a Knowledge Processing system using Improved Chaotic Associative Memory (KPICAM). The proposed KPICAM is based on an Improved Chaotic Associative Memory (ICAM) 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 ICAM makes use of this property in order to separate superimposed patterns and to deal with many-to-many associations. In this research, the ICAM is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed KPICAM 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.

Original languageEnglish
Pages579-584
Number of pages6
DOIs
Publication statusPublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 2000 Jul 242000 Jul 27

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period00/7/2400/7/27

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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