Global Exponential Stability of Periodic Solution for Competitive Neural Networks with Time-Varying and Distributed Delays on Time Scales
Received:May 17, 2013  Revised:October 12, 2013
Key Word: stability   competitive neural networks   delays   time scales.  
Fund ProjectL:Supported by the Fundamental Research Funds for the Central Universities (Grant No.JUSRP51317B) and the National Natural Science Foundation of China (Grant No.60875036).
Author NameAffiliation
Yang LIU Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China 
Yongqing YANG Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China 
Tian LIANG Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China 
Xianyun XU Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Jiangsu 214122, P. R. China 
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Abstract:
      In this paper, competitive neural networks with time-varying and distributed delays are investigated. By utilizing Lyapunov functional methods, the global exponential stability of periodic solutions of the neural networks is discussed on time scales. In addition, an example is given to illustrate the effectiveness of the theoretical results.
Citation:
DOI:10.3770/j.issn:2095-2651.2014.04.009
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