Robust synchronization of delayed neural networks based on adaptive control and parameters identification
Robust synchronization of delayed neural networks based on adaptive control and parameters identification
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DOI：
10.1016/j.chaos.2005.04.022
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发表时间：
2006-02
影响因子：
7.8
通讯作者：
Jin Zhou;Tianping Chen;L. Xiang
中科院分区：
文献类型：
--
作者：
Jin Zhou;Tianping Chen;L. Xiang
研究了全参数未知的时滞神经网络的同步动力学问题。利用泛函微分方程的不变性原理，将自适应控制和线性反馈与更新的律相结合，导出了基于不确定混沌延迟神经网络参数辨识的鲁棒同步判定的一些简单而通用的准则。结果表明，本文提出的方法进一步扩展了最近文献中提出的思想和技术，并且在实践中也很容易实现。将理论结果应用于典型的混沌延迟Hopfied神经网络，数值仿真也验证了该方法的有效性和可行性。
This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique.