网络首发:2017-07-09,
纸质出版:2017
移动端阅览
张治中, 彭世国. 随机变时滞神经网络的输入状态稳定性[J]. 广东工业大学学报, 2017, 34(4): 84-88.
Zhang Zhi-zhong, Peng Shi-guo. Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay[J]. 2017, 34(4): 84-88.
张治中, 彭世国. 随机变时滞神经网络的输入状态稳定性[J]. 广东工业大学学报, 2017, 34(4): 84-88. DOI: 10.12052/gdutxb.160010.
Zhang Zhi-zhong, Peng Shi-guo. Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay[J]. 2017, 34(4): 84-88. DOI: 10.12052/gdutxb.160010.
给出了一种新的稳定性判据∶基于线性矩阵不等式(LMI)的输入状态稳定性判据.基于模型变换,通过构造合适的Lyapunov函数,利用随机分析理论、伊藤(It's)公式和一些不等式方法,以线性矩阵不等式形式给出了随机变时滞神经网络的随机输入状态稳定的充分条件.然后通过数值算例说明所提出的方法有较小的保守性,表明所提出方法的有效性.
A new stability criterion based on the linear matrix inequality (LMI) input-to-state stability criterion is given. Based on model transformation
input-to-state stability of Stochastic neural networks with Time-Varying Delay is given a sufficient condition in form of linear matrix inequality by constructing appropriate Lyapunov-Krasovskii functional
stochastic analysis theory and It's formula and applying some inequality approach. Then
the proposed method is proven to be less conservative by illustrating with the numerical examples
which shows the effectiveness of the method.
刘勇健, 刘义建. 人工神经用于岩体工程的方法改进[J]. 广东工业大学学报, 2002, 19(1): 21-25.LIU Y J, LIU Y J. The improving methods of artificial neural network in Geo-techn-ical engineering[J]. Journal of Guangdong University and Technology, 2002, 19(1): 21-25.
ZHU Q X, CAO J D. Mean-square exponential input-to-state stability of stochastic delayed neural networks [J]. Neurocomputing, 2014. 131(0): 157-163.
SONTAG E D. Smooth stabilization implies coprime factorization [J]. IEEE Trans. Autom. Control, 1989. 34(4): 435-443.
BLYTHE S, MAO X R, LIAO X L. Stability of stochastic delay neural networks [J]. Journal of the Franklin Institute 2001. 338(0): 481-495.
胡宣达. 随机微分方程稳定性理论[M]. 南京:南京大学出版社, 1990.
MAO X R. Exponential Stability of Stochastic Differential Equations [M]. New York: Marcel Dekker Inc, 1994.
GRUNE L. Input-to-state dynamical stability and its Lyapunov function characterization. Automatic Control, IEEE Transactions on [J]. 2002. 47(9): 1499-1504.
TAI Z. Input-to-state stability for Lur’e stochastic distributed parameter control systems [J]. Applied Mathematics Letters, 2012. 25(4): 706-711.
AHN C K. Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay [J]. Information Sciences, 2010, 180(23): 4582-4594.
ALWAN MOHAMAD S, LIU Z, XIE W C. On design of robust reliable control and input-to-state stabilization of uncertain stochastic systems with state delay [J]. Communications in Nonlinear Science and Numerical Simulation, 2013. 18(4): 1047-1056.
HUANG H Y, DU Q S, KANG X B. Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays [J]. ISA Transactions, 2013. 52(6): 759-767.
江明辉, 廖晓昕. 变时滞随机微分方程的指数稳定性[J]. 数学物理学报, 2006. 6(6): 254-258.JIANG M H, LIAO X X. Exponential stability of stochastic differential equations with variable delays [J]. Journal of Mathematical Physics, 2006. 6(6): 254-258.
ZHU Q X, CAO J D, RAKKIYAPPAN R. Exponential input-to-state stability of stochastic Cohen–Grossberg neural networks with mixed delays [J]. Nonlinear Dynamics, 2015, 79(2): 1085-1098.
ZHOU D M, YU S H, ZHANG Z Q. New LMI-based conditions for global exponential stability to a class of Cohen–Grossberg BAM networks with delays [J]. Neurocomputing, 2013. 121(18): 512-522.
LI X L, WANG M R. Novel robust stability criteria for stochastic hopfield neural network with time-varying delays [M]. Neural Information Processing. Springer Berlin Heidelberg, 2012: 474-479.
ZHAO F, ZHANG Q L, YAN X G, et al. Stochastic input-to-state stability and H∞ filtering for a class of stochastic nonlinear systems [J]. Applied Mathematics & Computation, 2014, 227: 672-686.
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621