Controlling Chaos in a Neural Network Based on the Phase Space Constraint

HE, Dr Guo-guang and CAO, Prof. Zhi-tong and CHEN, Dr. Hong-ping and ZHU, Dr Ping (2003) Controlling Chaos in a Neural Network Based on the Phase Space Constraint. [Journal (Paginated)]

Full text available as:



The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network.

Item Type:Journal (Paginated)
Subjects:Computer Science > Dynamical Systems
ID Code:4538
Deposited By: He, Dr Guoguang
Deposited On:18 Sep 2005
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. CAO Zhitong,\The dynamic associative memory of chaotic neural", J. of Zhejiang

University (Natural Science Edition), Supplement: p. 330{335 (in Chinese), 1998.

2. K. Aihara, T. Takabe and M. Toyoda, \chaotic neural networks". Phys Lett 144(6{7),

333{340 (1990).

3. M. Adachi and K. Aihara \Associative dynamics in a chaotic neural network", Neural

Networks 10(1), 83{98 (1997).

4. E. R. Kobori, K. Ikoda and K. Nakayama, \A model of dynamic associative memory",

IEEE International Conference on Neural Networks Conference Proceedings 2, 804{809


5. He Guoguang and Cao Zhitong, \Controlling chaos in chaotic neural network", Acta

Physica Sinica 50(11), p. 2103{2107 (in Chinese), 2001.

6. E. Ott, C. Grebogi and J. A. Yorke, \Controlling chaos", Phys. Rev. Lett. 64(11),

p. 1196{1199 (1990).

7. Pyragas, K. \Continuous Control of Chaos by Self-Controlling Feedback", Phys. Lett.

A170, p. 421{428 (1992).

8. X. S. Luo, \Using phase space compression to control chaos and hyperchaos", Acta

Physica Sinica, 48(3), p. 402{406 (in Chinese), 1999.


Repository Staff Only: item control page