中国物理B ›› 2008, Vol. 17 ›› Issue (5): 1670-1677.doi: 10.1088/1674-1056/17/5/023

• GENERAL • 上一篇    下一篇

New results on global exponential stability of competitive neural networks with different time scales and time-varying delays

崔宝同, 陈 君, 楼旭阳   

  1. Research Center of Control Science and Engineering, Jiangnan University, Wuxi 214122, China
  • 收稿日期:2007-08-06 修回日期:2007-09-20 出版日期:2008-05-20 发布日期:2008-05-20
  • 基金资助:
    Project supported by National Natural Science Foundation of China (Grant No 60674026), the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016) and Program for Innovative Research Team of Jiangnan University of China.

New results on global exponential stability of competitive neural networks with different time scales and time-varying delays

Cui Bao-Tong(崔宝同), Chen Jun(陈君)†, and Lou Xu-Yang(楼旭阳)   

  1. Research Center of Control Science and Engineering, Jiangnan University, Wuxi 214122, China
  • Received:2007-08-06 Revised:2007-09-20 Online:2008-05-20 Published:2008-05-20
  • Supported by:
    Project supported by National Natural Science Foundation of China (Grant No 60674026), the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016) and Program for Innovative Research Team of Jiangnan University of China.

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摘要： This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.

Abstract: This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.

Key words: competitive neural network, different time scale, global exponential stability, delay

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

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