Title :
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
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;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279230
