A delay-decomposition approach for stability of neural network with time-varying delay
Qiu Fang(邱芳)a)b)†, Cui Bao-Tong (崔宝同)a), and Ji Yan(籍艳)a)
a College of Communications and Control Engineering, Jiangnan University, Wuxi 214122, China; b Department of Mathematics, Binzhou University, Binzhou 256603, China
Abstract This paper studies delay-dependent asymptotical stability problems for the neural system with time-varying delay. By dividing the whole interval into multiple segments such that each segment has a different Lyapunov matrix, some improved delay-dependent stability conditions are derived by employing an integral equality technique. A numerical example is given to demonstrate the effectiveness and less conservativeness of the proposed methods.
Received: 01 April 2009
Revised: 19 May 2009
Accepted manuscript online:
Fund: Project supported by the National
Natural Science Foundation of China (Grant No 60674026) and the
Natural Science Foundation of Jiangsu Province of China (Grant No
BK2007016).
Cite this article:
Qiu Fang(邱芳), Cui Bao-Tong (崔宝同), and Ji Yan(籍艳) A delay-decomposition approach for stability of neural network with time-varying delay 2009 Chin. Phys. B 18 5203
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.