Stability and stabilization for stochastic Cohen-Grossberg neural networks with impulse control and noise-induced control

被引:22
|
作者
Guo, Yingxin [1 ]
Cao, Jinde [2 ,3 ]
机构
[1] Qufu Normal Univ, Sch Math Sci, Qufu 273165, Peoples R China
[2] Sch Math, Nanjing, Jiangsu, Peoples R China
[3] Res Ctr Complex Syst & Network Sci, Nanjing, Jiangsu, Peoples R China
关键词
impulse control; linear matrix inequality; Lyapunov functionals; robustly globally asymptotic stability; stochastic Cohen-Grossberg neural network; GLOBAL ASYMPTOTIC STABILITY; SQUARE EXPONENTIAL STABILITY; PERIODIC-SOLUTIONS;
D O I
10.1002/rnc.4379
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
By applying the Ito formula, the Gronwall inequality, and the law of large numbers technique, a new simple sufficient inequality condition is presented for the almost surely exponential stability of the stochastic Cohen-Grossberg neural networks with impulse control and time-varying delays. Moreover, a new result is also given for the existence of unique states of the systems. An impulsive controller and a suitable noise controller are also given at the same time. The condition contains and improves some of the previous results in the earlier references.
引用
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页码:153 / 165
页数:13
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