Knowledge processing system using chaotic associative memory

Author

Osana, Yuko ; Hagiwara, Masafumi

Author_Institution

Keio Univ., Yokohama, Japan

Volume

2

fYear

1999

fDate

36342

Firstpage

746

Abstract

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, the input pattern is searched. The CAM makes use of this property in order to separate the 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; and 3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system

Keywords

chaos; content-addressable storage; knowledge based systems; knowledge representation; neural nets; chaotic associative memory; chaotic neurons; characteristics inheritance; knowledge processing system; knowledge representation; neural networks; semantic network; Associative memory; Biological neural networks; Biological system modeling; CADCAM; Chaos; Computer aided manufacturing; Computer simulation; Humans; Neurons; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831042

Filename

831042