时滞反馈Lorenz系统的神经网络滑模自适应同步控制及在保密通信中的应用

为了研究不确定Lorenz混沌系统同步控制在保密通信中的应用,首先设计了时滞反馈Lorenz混沌系统,并通过Poincare映射和功率谱分析了其混沌动力学特性.在此基础上,提出了不确定时滞反馈Lorenz混沌系统的神经网络滑模自适应同步控制策略.应用径向基(RBF)神经网络逼近混沌系统的不确定项,基于该径向基神经网络的输出再利用滑模控制和自适应控制相结合的方法提出了单维同步控制器的设计.最后,将所设计的同步控制方法应用于保密通信.仿真结果表明,本文所提出的神经网络滑模自适应同步控制方法可以实现混沌系统同步并可应用于保密通信,且具有较强的抗干扰能力.

Neural Network Sliding mode Adaptive Synchronization Control for Delayed Feedback Lorenz System and Its Application in Secure Communication

To study the synchronization control of uncertain Lorenz systems and its application in secure communication, the time-delay feedback Lorenz chaotic system is first designed and its chaotic dynamics is analyzed by the Poincare map and the power spectrum in this paper. And then, the neural network sliding-mode adaptive synchronization control scheme is proposed for the delayed feedback Lorenz chaotic system. The RBF neural network is used to approximate the system uncertainty. Combining with the output of neural networks, sliding mode control, adaptive control technique are applied to achieve a single dimension signal controller. On the basis of the proposed synchronization control scheme, the application in secure communication is developed. The experimental results show that the proposed neural network sliding-mode adaptive synchronization control scheme can realize chaotic synchronization and can be used in secure communication, which has strong anti-interference ability.