Yang Rongni, Gao Huijun, Shi Peng
Space Control and Inertial Technology Research Center, Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
IEEE Trans Syst Man Cybern B Cybern. 2009 Apr;39(2):467-74. doi: 10.1109/TSMCB.2008.2006860. Epub 2008 Dec 16.
In this paper, the problem of asymptotic stability for stochastic Hopfield neural networks (HNNs) with time delays is investigated. New delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional. Moreover, the results are further extended to the delayed stochastic HNNs with parameter uncertainties. The main idea is based on the delay partitioning technique, which differs greatly from most existing results and reduces conservatism. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.
本文研究了具有时滞的随机Hopfield神经网络（HNNs）的渐近稳定性问题。通过构造一个新颖的Lyapunov-Krasovskii泛函，给出了新的时滞依赖稳定性判据。此外，结果进一步扩展到具有参数不确定性的时滞随机HNNs。主要思想基于时滞划分技术，这与大多数现有结果有很大不同，并减少了保守性。提供了数值例子来说明所提出技术的有效性和较低的保守性。
