中国物理B ›› 2013, Vol. 22 ›› Issue (5): 50504-050504.doi: 10.1088/1674-1056/22/5/050504

• GENERAL • 上一篇    下一篇

Exponential synchronization of coupled memristive neural networks via pinning control

王冠, 沈轶, 尹泉   

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Wuhan 430074, China
  • 收稿日期:2012-09-07 修回日期:2012-10-31 出版日期:2013-04-01 发布日期:2013-04-01
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 61134012 and 11271146).

Exponential synchronization of coupled memristive neural networks via pinning control

Wang Guan (王冠), Shen Yi (沈轶), Yin Quan (尹泉)   

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Wuhan 430074, China
  • Received:2012-09-07 Revised:2012-10-31 Online:2013-04-01 Published:2013-04-01
  • Contact: Shen Yi E-mail:yishen64@163.com
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 61134012 and 11271146).

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摘要： This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled subsystems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

关键词: synchronization, memristor, coupled neural networks, pinning control

Abstract: This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled subsystems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

Key words: synchronization, memristor, coupled neural networks, pinning control

中图分类号:  (Control of chaos, applications of chaos)

  • 05.45.Gg
05.45.Xt (Synchronization; coupled oscillators)