Dynamic behaviors of memristor-based delayed recurrent networks

被引：1
作者：
Shiping Wen
Zhigang Zeng
Tingwen Huang
机构：
[1] Huazhong University of Science and Technology,Department of Control Science and Engineering
[2] Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China,undefined
[3] Texas A&M University at Qatar,undefined
来源：
Neural Computing and Applications | 2013年 / 23卷
关键词：
Memristor; Recurrent networks; Time delays;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.
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页码：815 / 821
页数：6
相关论文
共 80 条
[1]  
Chua L(1971)Memristor—the missing circuit element IEEE Trans Circuit T-18 507-519
[2]  
Strukov D(2008)The missing memristor found Nature 453 80-83
[3]  
Snider G(2009)Circuit elements with memory: memristors, memcapacitors, and meminductors Proc IEEE 97 1717-1724
[4]  
Stewart D(2002)An estimation of upperbound of delays for global asymptotic stability of delayed Hopfield neural networks IEEE Trans Circuits Syst I 49 1028-1032
[5]  
Williams R(2005)Global robust stability of delayed recurrent neural networks Chaos Solitions Fractals 23 221-229
[6]  
Ventra M(2006)Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays IEEE Trans Neural Netw 17 1646-1651
[7]  
Pershin Y(2005)Global asymptotic and robust stability of recurrent neural networks with time delays IEEE Trans Circuits Syst I 52 417-426
[8]  
Chua L(2002)Global asmptotic stability and global exponential stability of continuous-time recurrent neural networks IEEE Trans Automat Control 47 802-807
[9]  
Chen A(2003)On global asymptotic stability of recurrent neural networks with time-varying delays Appl Math Comput 142 143-154
[10]  
Cao J(2002)Global exponential stability and periodic solutions of recurrent neural networks with delays Phys Lett A 298 393-404
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