He Guo-guang, Cao Zhi-tong, Chen Hong-ping, Zhu Ping. Controlling Chaos in a Neural Network Based on the Orbit Space Compression[J]. Journal of Electronics & Information Technology, 2004, 26(1): 142-145.
Citation:
He Guo-guang, Cao Zhi-tong, Chen Hong-ping, Zhu Ping. Controlling Chaos in a Neural Network Based on the Orbit Space Compression[J]. Journal of Electronics & Information Technology, 2004, 26(1): 142-145.
He Guo-guang, Cao Zhi-tong, Chen Hong-ping, Zhu Ping. Controlling Chaos in a Neural Network Based on the Orbit Space Compression[J]. Journal of Electronics & Information Technology, 2004, 26(1): 142-145.
Citation:
He Guo-guang, Cao Zhi-tong, Chen Hong-ping, Zhu Ping. Controlling Chaos in a Neural Network Based on the Orbit Space Compression[J]. Journal of Electronics & Information Technology, 2004, 26(1): 142-145.
In this paper, a controlling chaos method of the orbit space compression is proposed for a Chaotic Neural Network(CNN). The computer simulation of the chaotic behaviors of the CNN proves that each pattern can be controlled using the orbit space compression. Starting from any initial state the CNN can converge in a stored pattern or its inverse pattern, which has the smallest Hamming distance with the initial state. The controlling method of the orbit space compression shows clear physical meaning and can be easily carried out.
曹志彤.混沌神经网络的动态联想记忆.浙江大学学报(自然科学版),1998,增刊:330-335.[2]Aihara K, Takabe T, Toyoda M. Chaotic neural networks[J].Phys. Lett.1990, 144(6,7):333-340[3]Adachi M, Aihara K. Associative dynamics in a chaotic neural network[J].Neural Networks.1997,10(1):83-98[4]Kobori E R, Ikoda K, Nakayama K. A model of dynamic associative memory. IEEE International Conference on Neural Networks Conference Proceedings, Washington, DC, USA, 1996, 2: 804-809.[5]何国光,曹志彤.混沌神经网络的信息搜素.物理学报,2001,50(11):2103-2107.[6]Ott E, Grebogi C, Yorke J A. Controlling chaos[J].Phys. Rev. Lett.1990, 64:1196-1199[7]罗晓曙.利用相空间压缩实现混沌与超混沌控制.物理学报,1999,48(3):402-407.
DownLoad: