计算机工程与应用2016，Vol.52Issue(16)：12-16,5.DOI:10.3778/j.issn.1002-8331.1603-0258
具有概率分布时滞的神经网络稳定性新判据
New stability criteria for neural networks with probabilistic time-varying delay
摘要
Abstract
Based on probability theory and the Lyapunov stability theory, the stability problem for a class of neural networks with probabilistic time-varying delay is studied. By constructing a proper Lyapunov-Krasovskii functional(KLF), and using Wirtinger-based inequality and the reciprocal convex technique to estimate the upper of the time derivative of the KLF, a novel sufficient criterion is derived to guarantee neural networks with time-varying delay to be asymptotically stable in the mean-square sense. The criterion formulated in terms of LMIs(Linear Matrix Inequalities)is dependent not only on the upper bound of the time delay but also on time delay’s probability distribution. Finally, two numerical examples are given to illustrate that the approach proposed in this paper is more effective and less conservative than some existing ones.关键词
时滞神经网络/概率时滞/渐近稳定/倒凸技术/线性矩阵不等式Key words
delayed neural networks/probabilistic time-varying delay/asymptotical stability/reciprocal convex technique/Linear Matrix Inequalities(LMIs)分类
信息技术与安全科学引用本文复制引用
张芬,张艳邦..具有概率分布时滞的神经网络稳定性新判据[J].计算机工程与应用,2016,52(16):12-16,5.基金项目
国家自然科学基金（No.61501388，No.11501482）；陕西省自然科学基金（No.2013JM1014）；陕西省教育厅科学研究基金（No.14JK1797）；咸阳师范学院高层次人才引进计划项目（No.14XSYK005）；咸阳师范学院科研基金资助项目（No.13XSYK009）。 （）
