Relaxed delay-dependent exponential stability condition for a class of neural networks with polytopic uncertainties and distributed delays

Relaxed delay-dependent exponential stability condition for a class of neural networks with polytopic uncertainties and distributed delays
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具有多面不确定性和分布式时滞的一类神经网络的宽松时滞相关指数稳定性条件

DOI:
10.1007/s11768-011-8159-x
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发表时间:
2011-06
期刊:
Journal of Control Theory and Applications
影响因子:
--
通讯作者:
Hongbin Zhang
Hongbin Zhang
中科院分区:
其他
文献类型:
--
作者:
Jianwei Xia;Hongbin Zhang

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研究了一类具有多面体不确定性和分布时滞的神经网络的全局鲁棒指数稳定性。利用参数依赖的Lypaunov-Krasovskii泛函和自由权矩阵,得到了保证神经网络平衡点鲁棒全局指数稳定的充分条件.导出的充分条件提出了一组宽松的线性矩阵不等式(LMI),这可以很容易地检查最近开发的算法求解LMI。数值算例验证了该准则的有效性。
The global robust exponential stability of a class of neural networks with polytopic uncertainties and distributed delays is investigated in this paper. Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability of the equilibrium point of the considered neural networks. The derived sufficient condition is proposed in terms of a set of relaxed linear matrix inequalities (LMIs), which can be checked easily by recently developed algorithms solving LMIs. A numerical example is given to demonstrate the effectiveness of the proposed criteria.
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