Robust exponential stability for interval neural networks with delays and non-Lipschitz activation functions
Robust exponential stability for interval neural networks with delays and non-Lipschitz activation functions
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DOI：
10.1007/s11071-010-9926-9
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发表时间：
2011-01
影响因子：
5.6
通讯作者：
Huaiqin Wu;Feng Tao;Leijie Qin;Rui Shi;Lijun He
中科院分区：
文献类型：
--
作者：
Huaiqin Wu;Feng Tao;Leijie Qin;Rui Shi;Lijun He
本文研究了具有时滞和逆Hölder神经元激活函数的区间神经网络的全局鲁棒指数稳定性。利用线性矩阵不等式（LMI）技巧和Brouwer度性质，证明了平衡点的存在唯一性。利用李雅普诺夫泛函方法，建立了该网络全局鲁棒指数稳定的充分条件。数值算例验证了理论结果的有效性。
In this paper, the global robust exponential stability of interval neural networks with delays and inverse Hölder neuron activation functions is considered. By using linear matrix inequality (LMI) techniques and Brouwer degree properties, the existence and uniqueness of the equilibrium point are proved. By applying Lyapunov functional approach, a sufficient condition which ensures that the network is globally robustly exponentially stable is established. A numerical example is provided to demonstrate the validity of the theoretical results.