摘要
基于概率理论和Lyapunov稳定性理论,研究一类具有概率分布时滞神经网络稳定性问题。通过构造合适的Lyapunov-Krasovskii(LK)泛函,运用Wirtinger不等式和倒凸技术来估计LK泛函导数的上界,得到了确保该类时滞神经网络在均方意义下的全局渐近稳定的新判据。该判据以LMIs形式表出,它不但依赖于时滞的上界,而且依赖于时滞的概率分布。给出两个数值例子,仿真表明所提方法的有效性和较弱的保守性。
Based on probability theory and the Lyapunov stability theory, the stability problem for a class of neural networkswith probabilistic time-varying delay is studied. By constructing a proper Lyapunov-Krasovskii functional(KLF), and usingWirtinger-based inequality and the reciprocal convex technique to estimate the upper of the time derivative of the KLF, anovel sufficient criterion is derived to guarantee neural networks with time-varying delay to be asymptotically stable inthe mean-square sense. The criterion formulated in terms of LMIs(Linear Matrix Inequalities)is dependent not only onthe upper bound of the time delay but also on time delay’s probability distribution. Finally, two numerical examples aregiven to illustrate that the approach proposed in this paper is more effective and less conservative than some existing ones.
作者
张芬
张艳邦
ZHANG Fen;ZHANG Yanbang(College of Mathematics and Information Science, Xianyang Normal University, Xianyang, Shaanxi 712000, China;School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China)
出处
《计算机工程与应用》
CSCD
北大核心
2016年第16期12-16,共5页
Computer Engineering and Applications
基金
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)
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
delayed neural networks
probabilistic time-varying delay
asymptotical stability
reciprocal convex technique
Linear Matrix Inequalities(LMIs)
