A novel chaotic neural network for many-to-many associations and successive learning

Author

Duan, Shukai ; Liu, Guanpuan ; Wang, Lidan ; Qiu, Yuhui

Author_Institution

Sch. of Electron. Inf. Eng., Southwest China Normal Univ., Chongqing, China

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

135

Abstract

In this paper, we propose a novel successive learning chaotic neural network (NSLCNN). It has two distinctive features: (1) it can deal with many-to-many associations; (2) it can learn unknown pattern successively. As for the first feature, when a stored pattern is given to the network, the network searches around the input pattern by chaos. The proposed model makes use of this property to deal with many-to-many associations. As for the second one, when a different input pattern is given, a different response is received. So it can distinguish unknown patterns from the known patterns and learn the unknown patterns successively. A series of computer simulations show the effectiveness of the proposed model.

Keywords

chaos; learning (artificial intelligence); neural nets; chaos; chaotic neural network; many-to-many association; successive learning; Associative memory; Biological neural networks; Biological system modeling; Chaos; Computer networks; Computer simulation; Information science; Neural networks; Neurons; Olfactory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on

Conference_Location

Nanjing

Print_ISBN

0-7803-7702-8

Type

conf

DOI

10.1109/ICNNSP.2003.1279230

Filename

1279230