具有未知时变时滞的区间Cohen-Grossberg神经网络的鲁棒稳定性分析

Robust stability analysis for interval cohen-grossberg neural networks with unknown time-varying delays.

作者信息

Zhang Huaguang, Wang Zhanshan, Liu Derong

机构信息

School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China.

出版信息

IEEE Trans Neural Netw. 2008 Nov;19(11):1942-55. doi: 10.1109/TNN.2008.2006337.

DOI:10.1109/TNN.2008.2006337

PMID:18990645

Abstract

In this paper, robust stability problems for interval Cohen-Grossberg neural networks with unknown time-varying delays are investigated. Using linear matrix inequality, M -matrix theory, and Halanay inequality techniques, new sufficient conditions independent of time-varying delays are derived to guarantee the uniqueness and the global robust stability of the equilibrium point of interval Cohen-Grossberg neural networks with time-varying delays. All these results have no restriction on the rate of change of the time-varying delays. Compared to some existing results, these new criteria are less conservative and are more convenient to check. Two numerical examples are used to show the effectiveness of the present results.

摘要

本文研究了具有未知时变延迟的区间Cohen-Grossberg神经网络的鲁棒稳定性问题。利用线性矩阵不等式、M矩阵理论和Halanay不等式技术，导出了与时变延迟无关的新的充分条件，以保证具有时变延迟的区间Cohen-Grossberg神经网络平衡点的唯一性和全局鲁棒稳定性。所有这些结果对时变延迟的变化率没有限制。与一些现有结果相比，这些新准则保守性较小，检验更方便。两个数值例子说明了本文结果的有效性。