Liu Pingzhou, Han Qing-long
School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, Qld 4702, Australia.
IEEE Trans Neural Netw. 2007 Sep;18(5):1343-55. doi: 10.1109/tnn.2007.891593.
This paper is concerned with the problem of local and global asymptotic stability for a class of discrete-time recurrent neural networks, which provide discrete-time analogs to their continuous-time counterparts, i.e., continuous-time recurrent neural networks with distributed delay. Some stability criteria, which include some existing results as their special cases, are derived. A discussion about the dynamical consistence of discrete-time neural networks versus their continuous-time counterparts is provided. An unconventional finite difference method is proposed and an example is also given to show the effectiveness of the method.
本文关注一类离散时间递归神经网络的局部和全局渐近稳定性问题，这类网络为其连续时间对应网络（即具有分布时滞的连续时间递归神经网络）提供了离散时间模拟。推导了一些稳定性准则，其中包括一些现有结果作为其特殊情况。给出了关于离散时间神经网络与其连续时间对应网络动力学一致性的讨论。提出了一种非常规有限差分方法，并给出一个例子以说明该方法的有效性。
