复杂时间序列的多尺度分析

为纪念郑哲敏先生仙逝一周年, 作者回忆在读研期间先生对自己的教诲, 介绍先生和自己在非线性科学领域的一些工作, 及后来这些工作如何被拓展并演变成一些非线性问题通用的分析方法. 具体的方法包括混沌时间序列分析的相空间最优重构, 混沌的直接动力学判据, 基于依赖于尺度的李雅普诺夫指数(SDLE)的多尺度分析及自适应分形分析. 特别, SDLE可以非常好地刻画已知的所有类的时间序列模型, 因此, 可以统一已知的各种复杂性的度量. 自适应分形分析基于自适应滤波, 能非常好地决定趋势, 包括各种震荡模式和回归分析的非线性回归曲线, 也能非常好地去噪, 并把时间序列分解成各种内在固有模式. 这些方法已被广泛用于自然科学、工程技术和社会科学的诸多领域. 它们特别适用于各领域(包括运维)的故障诊断, 生物医学数据的分析及不确定性的度量. 郑先生从不囿于他自己熟悉的领域, 而是随着时代发展不断拓展新的领域. 身处百年未遇之大变局时期, 我们必须发扬光大先生的这种求真务实和开拓进取的精神.      

负高斯曲率曲面薄壁管吸能特性研究

为设计出具备优良吸能特性的薄壁结构,提出一种新型负高斯曲率曲面圆形横截面薄壁管(negative Gaussian curvature surface circular tube, NGC-C)。利用经验证的有限元分析方法对其进行轴向动态冲击模拟,提取各项性能指标,借助复杂比例评估法(complex proportion assessment, COPRAS)将其与传统薄壁吸能结构进行了综合性能对比。采用拉丁超立方抽样法从设计空间中提取样本点并获取各样本点对应性能响应值,建立代理模型。基于该代理模型,借助改进非支配排序遗传算法(non-dominated sorting genetic algorithm, NSGA-Ⅱ)对其进行了多目标优化设计。结果表明:NGC-C综合性能优于传统薄壁吸能结构,经优化后比吸能提高了16.47%,有效压溃长度降低了12.40%,质量减少了20.18%。将负高斯曲率曲面形态引入薄壁管构型,能够提高薄壁管的耐撞性和轴向抗变形能力。     

第十五届全国原子与分子物理学术会议投稿论文:粒子在 Hénon-Heiles势中的逃逸动力学模拟

利用庞加莱截面和相空间轨迹方法对粒子在Hénon-Heiles势中的逃逸动力学进行了模拟。粒子的动力学性质敏感地依赖于粒子的能量。数值计算表明当能量很小时，粒子的运动是规则的；随着能量的增加，粒子的运动开始出现混沌。当能量增加到鞍点能 时,几乎所有的相空间轨迹都是混沌的。当粒子的能量 , 粒子可以越过势阱发生逃逸。对于给定的大于 的能量, 我们画出了粒子的逃逸－时间曲线和逃逸轨迹。我们的研究对于研究混沌传输和逃逸动力学具有一定的参考价值。     

A New 3D Four-Wing Chaotic System with Cubic Nonlinearity and Its Circuit Implementation

A new 3D four-wing smooth autonomous chaotic system in which each equation contains a cubic product term is presented and physically implemented. Spectral analysis shows that the four-wing chaotic attractor has extremely wide frequency bandwidth compared with that of the Lorenz system and other four-wing chaotic systems, which is important in some relevant engineering applications such as secure communications.     

Control of a Unified Chaotic System via Single Variable Feedback

Based on the LaSalle invariance principle, we propose a simple adaptive-feedback for controlling the unified chaotic system. We show explicitly with numerical proofs that our method can easily achieve the control of chaos in the unified chaotic system using only a single variable feedback. The present controller, to our knowledge, is the simplest control scheme for controlling a unified chaotic system.     

Synchronization of different chaotic systems via active radial basis functions sliding mode controller

This paper presents a new method to synchronize different chaotic systems with disturbances via an active radial basis function （RBF） sliding controller. This method incorporates the advantages of active control, neural network and sliding mode control. The main part of the controller is given based on the output of the RBF neural networks and the weights of these single layer networks are tuned on-line based on the sliding mode reaching law. Only several radial basis functions are required for this controller which takes the sliding mode variable as the only input. The proposed controller can make the synchronization error converge to zero quickly and can overcome external disturbances. Analysis of the stability for the controller is carried out based on the Lyapunov stability theorem. Finally, five examples are given to illustrate the robustness and effectiveness of the proposed synchronization control strategy.     

Hash function based on the generalized Henon map

A new Hash function based on the generalized Henon map is proposed. We have obtained a binary sequence with excellent pseudo-random characteristics through improving the sequence generated by the generalized Henon map, and use it to construct Hash function. First we divide the message into groups, and then carry out the Xor operation between the ASCII value of each group and the binary sequence, the result can be used as the initial values of the next loop. Repeat the procedure until all the groups have been processed, and the final binary sequence is the Hash value. In the scheme, the initial values of the generalized Henon map are used as the secret key and the messages are mapped to Hash values with a designated length. Simulation results show that the proposed scheme has strong diffusion and confusion capability, good collision resistance, large key space, extreme sensitivity to message and secret key, and it is easy to be realized and extended.     

Passive adaptive control of chaos in synchronous reluctance motor

The performance of synchronous reluctance motor （SynRM） degrades due to chaos when its systemic parameters fall into a certain area. To control the undesirable chaos in SynRM, a passive control law is presented in this paper, which transforms the chaotic SynRM into an equivalent passive system. It is proved that the equivalent system can be asymptotically stabilized at the set equilibrium point, namely, chaos in SynRM can be controlled. Moreover, in order to eliminate the influence of undeterministic parameters, an adaptive law is introduced into the designed controller. Computer simulation results show that the proposed controller is very effective and robust against the uncertainties in systemic parameters. The present study may help to maintain the secure operation of industrial servo drive system.     

Controlling chaos based on an adaptive nonlinear compensator mechanism

The control problems of chaotic systems are investigated in the presence of parametric uncertainty and persistent external disturbances based on nonlinear control theory. By using a designed nonlinear compensator mechanism, the system deterministic nonlinearity, parametric uncertainty and disturbance effect can be compensated effectively. The renowned chaotic Lorenz system subjected to parametric variations and external disturbances is studied as an illustrative example. From the Lyapunov stability theory, sufficient conditions for choosing control parameters to guarantee chaos control are derived. Several experiments are carried out, including parameter change experiments, set-point change experiments and disturbance experiments. Simulation results indicate that the chaotic motion can be regulated not only to steady states but also to any desired periodic orbits with great immunity to parametric variations and external disturbances.     

Robust adaptive synchronization of chaotic neural networks by slide technique

In this paper, we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay. In order to increase the robustness of the two coupled neural networks, the key idea is that a sliding-mode-type controller is employed. Moreover, without the estimate values of the network unknown parameters taken as an updating object, a new updating object is introduced in the constructing of controller. Using the proposed controller, without any requirements for the boundedness, monotonicity and differentiability of activation functions, and symmetry of connections, the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are. Finally, the numerical simulation validates the effectiveness and feasibility of the proposed technique.