Adaptive synchronization between two non-identical BAM neural networks with unknown parameters and time-varying delays

被引：0
作者：
Mostafa Zarefard
Sohrab Effati
机构：
[1] International Campus,Department of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of Mashhad
[2] Ferdowsi University of Mashhad,Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP)
来源：
International Journal of Control, Automation and Systems | 2017年 / 15卷
关键词：
Adaptive synchronization; bidirectional associative memory neural networks; Lyapunov stability; time-varying; unknown parameters;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, the synchronization of two non-identical bidirectional associative memory (BAM) neural networks with unknown parameters and time-varying delays is investigated. Two adaptive controllers are designed to guarantee the global asymptotic synchronization of state trajectories for two non-identical BAM neural networks. Lyapunov stability theory and Barbalat’s lemma are used to guarantee the synchronization of response and drive systems. Finally, an illustrative example is given to demonstrate the effectiveness of the presented synchronization scheme.
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页码：1877 / 1887
页数：10
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