Adaptive exponential synchronization of delayed Cohen-Grossberg neural networks with discontinuous activations

被引:18
|
作者
Wu, Huaiqin [1 ]
Zhang, Xiaowei [1 ]
Li, Ruoxia [1 ]
Yao, Rong [1 ]
机构
[1] Yanshan Univ, Dept Appl Math, Qinhuangdao 066001, Peoples R China
关键词
Neural networks; Exponential synchronization; Discontinuous neuron activations; Adaptive controller; Lyapunov stability theory; ALMOST-PERIODIC SOLUTION; TIME-VARYING DELAYS; GLOBAL CONVERGENCE; STABILITY; SYSTEMS;
D O I
10.1007/s13042-014-0258-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper treats of the exponential synchronization issue of delayed Cohen-Grossberg neural networks with discontinuous activations. By utilizing Lyapunov stability theory, an adaptive controller is designed such that the response system can be exponentially synchronized with a drive system. Our synchronization criteria are easily verified and the obtained results are also applicable to neural networks with continuous activations since they are a special case of neural networks with discontinuous activations. Results of this paper improve a few previous known results. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.
引用
收藏
页码:253 / 263
页数:11
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