混沌系统的同步与反同步同时存在问题研究

本文研究了混沌系统的同步与反同步同时存在问题。首先,通过研究误差混沌系统的动力学性质,得到了一个混沌系统同步和反同步共同存在的充分条件。然后,应用自适应控制方法,得到了相应的自适应控制器。最后,数值仿真的结果验证了上述结果的正确性和有效性。     

基于信息隐藏的混沌JPEG图像加密算法

结合图像数据冗余性特点和图像压缩过程给出一种JPEG图像加密算法.在加密过程中：一方面结合压缩方法的特性,构造了加密算法,使得加密尽量不影响压缩;另一方面引入128bit的随机数,每次加密该随机数均不同.加密完成后,将该随机数嵌入到加密后图像的最低有效位后得到最终的密文图像.实验和分析表明该算法具有较好的安全性.此外,虽然加密过程导致图像DCT系数的损失,但并不影响解密后图像的视觉效果.     

小波变换实现5涡卷超混沌吸引子的同步

为了节约通信信道资源,采用小波变换去掉反馈变量的细节信息从而压缩其频带的方法实现了两个5涡卷超混沌吸引子的同步。在发送端,将系统的z变量通过小波滤波器滤出高频成分,将低频成分重构后发送到接收端,接收端将该信号作为反馈信号,实现了收发系统的同步。应用本同步方法不仅可以节约信道资源,同时,由于发送的信号已不是完整的混沌信号,也提高了系统的保密性。     

Spatial navigation model based on chaotic attractor networks

We present a model of spatial navigation based on the non-convergent dynamics of brain activity. The system includes a hippocampal module that processes global spatial information and a cortical module that deals with local sensory information. We test the model using several spatial navigation paradigms: goal finding, shortcutting and detouring. Computer simulations show that the performance of the agent qualitatively matches that of animals and related models. This new approach provides a novel interpretation of how the brain accomplishes spatial navigation.     

改进多种群差分进化算法的混沌系统参数估计

针对混沌系统参数估计的多峰寻优问题,提出一种改进的多种群差分进化算法。改进差分进化算法的变异操作,使其前期更适合全局性搜索,利用α核心集对当前种群进行聚类,分别对聚类后的子群选用贪婪的差分变异算子完成深度搜索,比较所选取各子群的最优值,得到全局最优值作为是否结束搜索的判断依据,并将其应用到混沌系统参数估计中。实验结果表明,该算法对于多峰值、大空间的全局性参数估计在收敛速度、精度上优于混合量子进化算法、改进粒子群优化算法以及DE/best/2算法。     

基于混沌时间序列理论的发动机状态参数降噪研究

针对因缺乏未染噪数据而难以考查航空发动机状态参数降噪效果的问题,提出了基于混沌时间序列理论来考查其降噪效果的办法,并结合实例考查了小波、奇异谱分析和局部投影3种降噪方法对航空发动机状态参数的降噪效果.仿真结果表明,局部投影法更多地保留了发动机状态参数的混沌特性,适用于航空发动机状态参数的降噪处理.     

一种基于混沌的数字图像加密算法

提出了一种基于Logisitic映射的混沌图像加密算法,为了提高算法的安全性,采用循环移位进一步扰乱图像像素值,得到最终的加密图像。实验证明该算法具有易于实现,计算速度快、密钥空间大、可以抵抗唯密文攻击等优点。     

一种改进的混沌跳频序列的设计方法

现有混沌跳频序列在混沌迭代次数、通信保密性和安全性等方面存在着不足.为解决上述问题,本文通过给出优化的Logistic映射函数,采用了一种改进的利用伪随机序列控制比特抽取来生成混沌跳频序列的方法.该方法各项跳频性能指标与已有研究成果相当,但其混沌迭代次数减少,序列安全性增强.     

Global dynamics for non-autonomous reaction-diffusion neural networks with time-varying delays

In this paper, a class of non-autonomous reaction-diffusion neural networks with time-varying delays is considered. Novel methods to study the global dynamical behavior of these systems are proposed. Employing the properties of diffusion operator and the method of delayed inequalities analysis, we investigate global exponential stability, positive invariant sets and global attracting sets of the neural networks under consideration. Furthermore, conditions sufficient for the existence and uniqueness of periodic attractors for periodic neural networks are derived and the existence range of the attractors is estimated. Finally two examples are given to demonstrate the effectiveness of these results.     

Intelligent adaptive control for MIMO uncertain nonlinear systems

This paper investigates an intelligent adaptive control system for multiple-input–multiple-output (MIMO) uncertain nonlinear systems. This control system is comprised of a recurrent-cerebellar-model-articulation-controller (RCMAC) and an auxiliary compensation controller. RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and RCMAC. Finally, two MIMO uncertain nonlinear systems, a mass–spring–damper mechanical system and a Chua’s chaotic circuit, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness.