Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays

被引：0
作者：
Jianying Xiao
Shouming Zhong
Fang Xu
机构：
[1] Southwest Petroleum University,School of Sciences
[2] University of Electronic Science and Technology of China,School of Mathematical Sciences
来源：
Advances in Difference Equations | / 2018卷
关键词：
Memristor; Disturbance attenuating control; Filippov; Time-varying delays; Lyapunov–Razumikhin Theorem;
D O I：
暂无
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学科分类号：
摘要：
This paper investigates the design of disturbance attenuating controller for memristive recurrent neural networks (MRNNs) with mixed time-varying delays. By applying the combination of differential inclusions, set-valued maps and Lyapunov–Razumikhin, a feedback control law is obtained in the simple form of linear matrix inequality (LMI) to ensure disturbance attenuation of memristor-based neural networks. Finally, a numerical example is given to show the effectiveness of the proposed criteria.
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[1]  
Cao J.D.(2005)Global robust stability of delayed recurrent neural networks Chaos Solitons Fractals 23 221-229
[2]  
Huang D.(2005)Global asymptotic and robust stability of recurrent neural networks with time delays IEEE Trans. Circuits Syst. I 52 417-426
[3]  
Qu Y.(2006)Global robust stability analysis of neural networks with multiple time delays IEEE Trans. Circuits Syst. I 53 166-176
[4]  
Cao J.D.(2012)Robust stability analysis of a class of neural networks with discrete time delays Neural Netw. 29–30 52-59
[5]  
Wang J.(2006)Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli Neural Netw. 19 1528-1537
[6]  
Ozcan N.(2005)Global asymptotic stability and global exponential stability of neural networks with unbounded time-varying delays IEEE Trans. Circuits Syst. II, Express Briefs 52 168-173
[7]  
Arik S.(2008)An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays IEEE Trans. Neural Netw. 19 528-531
[8]  
Faydasicok O.(2008)Semi-global finite-time observers for nonlinear systems Automatica 44 3152-3156
[9]  
Arik S.(1971)Memristor—The missing circuit element IEEE Trans. Circuit Theory 18 507-519
[10]  
Zeng Z.G.(2008)The missing memristor found Nature 453 80-83
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