Relaxed Stability Conditions for Delayed Recurrent Neural Networks with Polytopic Uncertainties
Relaxed Stability Conditions for Delayed Recurrent Neural Networks with Polytopic Uncertainties
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DOI：
10.1142/s0129065706000871
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发表时间：
2006-12
影响因子：
8
通讯作者：
Baoyong Zhang;Shengyuan Xu;Y. Zou
中科院分区：
文献类型：
--
作者：
Baoyong Zhang;Shengyuan Xu;Y. Zou
本文研究了具有时变延迟和多面不确定性的循环神经网络的稳定性分析问题。采用参数相关的 Lypaunov 泛函来获得充分的条件，保证所考虑的神经网络平衡点的鲁棒全局指数稳定性。导出的稳定性标准以一组松弛的线性矩阵不等式表示，可以使用商用软件轻松测试。提供了两个数值例子来证明所提出的结果的有效性。
This paper investigates the problem of stability analysis for recurrent neural networks with time-varying delays and polytopic uncertainties. Parameter-dependent Lypaunov functionals are employed to obtain sufficient conditions that guarantee the robust global exponential stability of the equilibrium point of the considered neural network. The derived stability criteria are expressed in terms of a set of relaxed linear matrix inequalities, which can be easily tested by using commercially available software. Two numerical examples are provided to demonstrate the effectiveness of the proposed results.