Global exponential stability of mixed discrete and distributively delayed cellular neural network

doi:10.1088/1674-1056/20/1/010701

Abstract This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov–Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.

Keywords: global exponential stability cellular neural network mixed discrete and distributed delays Lyapunov–Krasovskii functional and Young inequality

Received: 17 April 2010
Revised: 18 June 2010
Accepted manuscript online:

PACS:

07.05.Mh

(Neural networks, fuzzy logic, artificial intelligence)

Fund: Project supported by the National Natural Science Foundations of China (Grant No. 70871056), the Society Science Foundation from Ministry of Education of China (Grant No. 08JA790057) and the Advanced Talents' Foundation and Student's Foundation of Jiangsu University, China (Grant Nos. 07JDG054 and 07A075).

Cite this article:

Yao Hong-Xing(姚洪兴) and Zhou Jia-Yan(周佳燕) Global exponential stability of mixed discrete and distributively delayed cellular neural network 2011 Chin. Phys. B 20 010701

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Global exponential stability of mixed discrete and distributively delayed cellular neural network

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