Knowledge processing system using improved chaotic associative memory

Author

Osana, Yuko ; Hagiwara, Masafumi

Author_Institution

Keio Univ., Yokohama, Japan

Volume

5

fYear

2000

fDate

2000

Firstpage

579

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

Keywords

content-addressable storage; knowledge representation; chaotic associative memory; chaotic neural network; chaotic neurons; computer simulations; improved chaotic associative memory; knowledge processing system; semantic network; Associative memory; Biological neural networks; Biological system modeling; CADCAM; Chaos; Computer aided manufacturing; Humans; Neural networks; Neurons; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.861531

Filename

861531