Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems
Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems
复制标题
DOI：
10.1016/j.chaos.2008.10.003
复制
发表时间：
2009-09
影响因子：
7.8
通讯作者：
Mou Chen;Wen‐Hua Chen
中科院分区：
文献类型：
--
作者：
Mou Chen;Wen‐Hua Chen
针对两个具有输入时滞和不确定性的混沌系统，提出了一种鲁棒自适应神经网络同步控制器。所研究的混沌系统可能具有广泛的非线性时滞输入不确定性。采用径向基函数（RBF）神经网络，通过适当的权值更新规律逼近时延不确定性的未知连续有界函数项。利用RBF神经网络的输出，提出了一种针对时滞不确定混沌系统的鲁棒自适应同步控制方案。最后，通过仿真实例验证了所提同步控制方案的有效性。
In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme.