带随机扰动的参数不确定混沌神经网络的同步

Synchronization of Chaotic Neural Networks with Stochastic Perturbation and Parameter Uncertainty

基于驱动-响应混沌同步原理,在同时考虑系统内部参数不确定性和系统外部随机干扰的情况下,研究了一类混沌神经网络的同步控制问题。利用随机微分方程理论,Lyapunov稳定性理论和线性矩阵不等式(LMI)方法,得出了混沌神经网络全局渐近同步的充分条件和控制器设计方法。所得的充分条件以线性矩阵不等式的形式给出,容易被已有的LMI工具箱所以验证,而且所设计的控制器结构简单,易于实现。最后,仿真实例验证了文中方法的有效性。

Based on drive-response synchronization principle, the synchronization control for a class of chaotic neural networks with stochastic perturbation and parameter uncertainty was invertigated. By using stochastic differential equation theory, Lyapunov stability theory and LMI approach, some sufficient conditions were obtained to guarantee the global synchronization of two chaotic neural networks and a procedure to construct a synchronization controller was proposed. The derived conditions were expressed in terms of linear matrix inequalities （LMIs） and were easy to verify via the LMI toolbox. The controller thus designed is simple and easy to implement. Finally, an example shows the effectiveness of the obtained results.