New global exponential stability criteria for interval-delayed neural networks
New global exponential stability criteria for interval-delayed neural networks
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DOI：
10.1243/09596518jsce1066
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
Xiaojie Su;Zhicheng Li;Yunkai Feng;Ligang Wu
中科院分区：
文献类型：
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作者：
Xiaojie Su;Zhicheng Li;Yunkai Feng;Ligang Wu
研究了一类时滞区间细胞神经网络的全局鲁棒指数稳定性问题。通过引入一种新的Lyapunov-Krasovslii函数并结合时滞分解的思想，利用线性矩阵不等式的形式，得到了区间时滞细胞神经网络全局指数稳定的时滞依赖条件.此外，随着分数变薄，保守性可以显著降低。最后通过数值算例说明了所得结果的优越性.
This paper is concerned with the problem of global robust exponential stability analysis for a class of interval cellular neural networks with time delay. By introducing a novel Lyapunov-Krasovslii function combined with the idea of delay fractioning, some delay-dependent conditions are derived in terms of the linear matrix inequality, which guarantee the considered interval delayed cellular neural networks to be globally exponentially stable. Moreover, the conservatism can be notably reduced as the fractioning becomes thinner. Some numerical examples are provided to demonstrate the advantages of the proposed results.