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

页码：253 / 263

页数：11

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