Chaotic Temporal Variability of Magnetospheric Radio Emissions

Nonthermal magnetospheric radio emissions provide the radio signatures of solar-terrestrial connection and may be used for space weather forecasting. A three-wave model of auroral radio emissions at the fundamental plasma frequency was proposed by Chian et al. (1994) involving resonant interactions of Langmuir, whistler and Alfvén waves. Chaos can appear in the nonlinear evolution of this three-wave process in the magnetosphere. We discuss two types of intermittency in radio signals driven by temporal chaos: the type-I Pomeau-Manneville intermittency and the interior crisis-induced intermittency. Examples of time series for both types of intermittency are presented. This revised version was published online in August 2006 with corrections to the Cover Date.     

Methods and advances in the study of aeroelasticity with uncertainties

Uncertainties denote the operators which describe data error, numerical error and model error in the mathematical methods. The study of aeroelasticity with uncertainty embedded in the subsystems, such as the uncertainty in the modeling of structures and aerodynamics, has been a hot topic in the last decades. In this paper, advances of the analysis and design in aeroelasticity with uncertainty are summarized in detail. According to the non-probabilistic or probabilistic uncer- tainty, the developments of theories, methods and experiments with application to both robust and probabilistic aeroelasticity analysis are presented, respectively. In addition, the advances in aeroelastic design considering either probabilistic or non-probabilistic uncertainties are introduced along with aeroelastic analysis. This review focuses on the robust aeroelasticity study based on the structured singular value method, namely the ~t method. It covers the numerical calculation algo- rithm of the structured singular value, uncertainty model construction, robust aeroelastic stability analysis algorithms, uncertainty level verification, and robust flutter boundary prediction in the flight test, etc. The key results and conclusions are explored. Finally, several promising problems on aeroelasticity with uncertainty are proposed for future investigation.     

子波变换平滑的混沌信号检测

将近几年兴起的子波变换、分形理论应用于带观测噪声混沌信号的检测。基于快速子波变换导出了平滑算子，将其对时序信号进行处理，并根据它在一定的尺度范围内具有标度不变的性质，利用改进的Ｇ－Ｐ算法计算其分数维数，对带有观测噪声的Ｌｏｒｅｎｚ吸引子波的混沌运动进行了检测。实验结果表明，该方法具有较好检测混沌现象的能力。     

Dynamical Systems Approach to Space Environment Turbulence

Space plasmas are dominated by waves, instabilities and turbulence. Dynamical systems approach offers powerful mathematical and computational techniques to probe the origin and nature of space environment turbulence. Using the nonlinear dynamics tools such as the bifurcation diagram and Poincaré maps, we study the transition from order to chaos, from weak to strong chaos, and the destruction of a chaotic attractor. The characterization of the complex system dynamics of the space environment, such as the Alfvén turbulence, can improve the capability of monitoring Sun-Earth connections and prediction of space weather. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于混沌特性的网络流量预测

高速网络中存在着以自相似为特征的多种业务流量,这种自相似特征和混沌现象的吸引子有着紧密的联系。本文基于混沌时间序列重构相空间理论,根据最大Lyapunov指数,分别采用W o lf原始算法和改进算法,对高速网络中自相似信源的速率进行了预测,并给出了最大可预报时间。仿真结果表明,W o lf改进算法预测精度及可靠性更高。     

基于改进实数遗传算法的函数全局优化

将一种改进的实数遗传算法用于函数全局优化。改进的算法建立在对基本实数遗传算法搜索特性判断的基础上。文中对实数遗传算法的基本操作进行了简单的讨论和选择 ,将一种混沌序列作为刺激因素加入到算法中 ,并将区域划分与取舍的思想应用到算法结构改进中。数值实验显示 ,新方法对寻找复杂问题的全局解、提高搜索精度方面较基本实数遗传算法有较大改进。     

混沌电路的传感应用

将混沌理论与电路理论相结合，分析了一些能产生混沌的电路，对帐篷映射迭代产生混沌的基本原理和性质进行了研究。并在仿真试验的基础上，设计出一种基于帐篷映射的差分混沌电路。根据混沌的初值敏感性以及输入值和混沌轨道的一一对应性，在理论上提出并论证了利用符号动力学的基本理论度量被测信号的可能性，将混沌电路应用于传感器设计，并成功地完成了基本模型试验，给出了基本的实验数据及其分析，开拓了混沌理论应用的新领域。     

时间序列关联维数在非线性系统运动性态识别中的应用

 在非线性动力系统维数较高、数学模型难以建立时, 利用时间序列分维数对系统的动力学性质进行了研究。通过对一经典非线性方程的分析, 得出利用随参数变化的时间序列分维数图, 可以很好地识别非线性系统从确定性状态到分叉或浑沌状态的临界参数点或区域。最后将此方法应用于一单盘Jeffcott 转子模型的分叉参数点识别及一转子运动状态识别, 得到了比较满意的结果。     

基于改进启发式蚁群算法的无人机自主航迹规划

无人机自主航迹规划是未来无人机作战使用的关键技术难题。针对传统航迹规划方法存在的求解效率不高、实时性较差、容易陷入局部最优等缺点,提出一种基于改进启发式蚁群算法的无人机航迹规划。算法前期使用Dijkstra 算法进行初始化航迹,引入启发式信息,提高搜索效率;采用Logistic 混沌映射初始化信息素,增加解的多样性,提高算法收敛速度;算法中、后期采用多航迹选择策略和模拟退火机制,提高全局搜索能力,避免因收敛速度过快,陷入局部最优解。对该算法进行仿真分析,结果表明：在存在威胁和障碍的复杂环境中,本文的改进蚁群算法与标准蚁群算法相比,能够有效规划出一条从起点到终点的航迹,并且寻优精度更高,收敛速度更快,具有一定应用价值。     

基于卷积神经网络和多项式混沌方法的翼型鲁棒性优化

在常规的翼型优化设计方法中,设计点处最优翼型的气动性能会在非设计点处有所恶化,因此有必要对翼型鲁棒性优化方法进行研究。提出一种基于卷积神经网络和多项式混沌方法的翼型鲁棒性设计方法,首先搭建基于卷积神经网络的气动力预测模型;其次采用多项式混沌方法对马赫数和攻角进行不确定度量化,构建翼型鲁棒性气动优化设计系统;最后对以 RAE2822 翼型为基准翼型的气动优化设计问题进行优化设计验证。结果表明：本文提出的翼型鲁棒性设计方法可行,优化后翼型的气动性能和鲁棒性气动优化设计效率在较宽的设计范围内都有所提升。