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
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2015年 / 6卷 / 02期
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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