Global Uniform Asymptotic Stability of Memristor-based Recurrent Neural Networks with Time Delays

被引:0
作者
Hu, Jin [1 ]
Wang, Jun [1 ]
机构
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年
关键词
EXPONENTIAL STABILITY; VARYING DELAYS; VARIABLE DELAYS; GENERAL-CLASS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Memristor is a newly prototyped nonlinear circuit device. Its value is not unique and changes according to the value of the magnitude and polarity of the voltage applied to it. In this paper, a simplified mathematical model is proposed to characterize the pinched hysteretic feature of the memristor, a memristor-based recurrent neural network model is given, and its global stability is studied. Using differential inclusion, two sufficient conditions for the global uniform asymptotic stability of memristor-based recurrent neural networks are obtained.
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页数:8
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