网络化神经网络的时滞依赖稳定性判据

Delay-dependent stability criteria for network-based neural networks

中文关键词  神经网络  采样控制  稳定性条件

英文关键词  neural networks (NNs)  sampled-data control  stability criteria

中文摘要

      本文研究了网络化神经网络的稳定性问题. 首先, 为了利用网络系统的采样特征, 定义了一个新的Lyapunov泛函; 通过分析网络诱导时延和执行周期之间的关系, 采用一个迭代凸组合技术, 得到了一个包含较少保守性的稳定性判据. 然后, 给出一个基于采样数据的神经网络稳定性判据, 减少了计算复杂性. 最后, 通过一个数例, 验证了本文方法的有效性和优越性.

英文摘要

      This paper investigates the problem of stability of network-based neural networks (NNs). To exploit the sampling characteristic of network systems, we define a new type of Lyapunov functional. By analyzing the relation between the network-induced delay and the executive duration, and employing an iterative convex combination technique, we develop a less conservative stability criterion for network-based NNs. To reduce the computational complexity, we also propose a stability criterion for sampled-data-based NNs. An illustrative example is given to show the effectiveness and the advantages of the proposed method.