DocumentCode :
814491
Title :
Chaotifying linear Elman networks
Author :
Li, Xiang ; Chen, Guanrong ; Chen, Zengqiang ; Yuan, Zhuzhi
Author_Institution :
Dept. of Autom., Nankai Univ., Tianjin, China
Volume :
13
Issue :
5
fYear :
2002
fDate :
9/1/2002 12:00:00 AM
Firstpage :
1193
Lastpage :
1199
Abstract :
A linear model of recurrent neural networks, called the Elman networks, is combined with the simple nonlinear modulo (mod) operation on its linear activated function so as to generate chaos purposely. Conditions on the weight matrix are obtained, under which the generated chaos satisfies the mathematical definition of chaos in the sense of T.Y. Li and J.A. Yorke (1975). Some simple and representative weight matrices are constructed for designing such Elman networks that can generate Li-Yorke chaos. Several numerical simulations are shown to verify and visualize the design.
Keywords :
chaos; matrix algebra; recurrent neural nets; Li-Yorke chaos; chaos; chaotification; linear Elman networks; linear activated function; linear model; mathematical definition; recurrent neural networks; simple nonlinear modulo operation; weight matrices; weight matrix; Automation; Biological neural networks; Chaos; Humans; Neurofeedback; Neurons; Numerical simulation; Recurrent neural networks; State feedback; Visualization;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2002.1031950
Filename :
1031950
Link To Document :
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