这篇论文与变化时间的延期为静态的周期性的神经网络(RNN ) 涉及稳定性分析。由 Lyapunov 功能的方法和线性矩阵不平等技术,一些新延期依赖者条件被建立保证神经网络的 asymptotic 稳定性。在线性矩阵不平等(LMI ) 表示了,建议延期依赖者稳定性条件能用最近发达的算法被检查。一个数字例子被给证明获得的条件能比一些存在的提供不太保守的结果。
This paper is concerned with the stability analysis for static recurrent neural networks (RNNs) with time-varying delay. By Lyapunov functional method and linear matrix inequality technique, some new delay-dependent conditions are established to ensure the asymptotic stability of the neural network. Expressed in linear matrix inequalities (LMIs), the proposed delay-dependent stability conditions can be checked using the recently developed algorithms. A numerical example is given to show that the obtained conditions can provide less conservative results than some existing ones.