地震激励下深水桥墩的随机振动分析

深水桥墩属于一类浸没在深水区域中的细长结构,在地震激励下不能忽略动水压力效应以及非线性效应,又鉴于地震激励的随机特性,因而地震激励下深水桥墩的动力学问题可归结为一个非线性随机振动问题,需开展非线性随机振动研究.为此,本文首先建立深水桥墩的非线性随机动力学模型,包括:将桥墩简化为固定在岩石地基上的悬臂梁;采用Kanai-Tajimi过滤白噪声模型模拟地震动加速度过程;利用辐射波浪理论描述动水压力以及凯恩方法推导深水桥墩系统的运动方程.随后,运用标准随机平均法将系统运动方程近似为关于幅值的平均伊藤方程,建立并求解相应的FPK方程以获得该系统的近似稳态概率密度函数.最后,分别考察了激励强度,质量比以及浸入比对系统稳态响应的影响,并采用蒙特卡罗模拟验证理论解析解的有效性.结果表明:理论解析解与蒙特卡罗模拟结果相当吻合,理论解析解具有较高的精度;激励强度的增强、质量比的增加以及浸入比的提高均会对桥墩的稳态响应产生负面影响,即放大了系统的响应;动水压力效应降低了质量比对系统响应的影响程度;桥墩内域水体的存在也会放大系统响应,且这种影响会随着浸没比的增加而增加.本文的研究结果对于深水桥墩的抗震优化设...     

机械系统状态监测与故障诊断领域的重要课题

非线性是机械系统动力学的固有属性。当非线性因素较为突出时，基于线性振动理论的机械故障诊断与预测方法不仅导致定是的误差，更重要的是，将与故障紧密相关的丰富的系统非线性动力学行为。本文指出了基于线怀振动的机械故障诊断与预测方法的局限性；分析了机械系统非线性振动的特征及几种典型的非线性故障现象；讨论了机械设备非线性故障的机理、监测、诊断与预测的有关问题。本文旨在引起对机械系统非线性振动特征及由此而产生的     

信息融合非线性控制理论初探

将信息融合思想引入控制领域,提出信息融合非线性控制理论．基于信息融合最优估计理论,研究非线性离散控制系统的最优控制问题．信息融合控制是把对控制系统的所有性能要求以及系统动力学转化为可融合信息,然后从信息融合估计的角度,使原问题转化为求控制量的“融合估计”问题．首先,给出了非线性信息融合估计定理;然后,推导了非线性系统信息融合控制的算法;最后,通过对机械手的转移控制问题的仿真研究说明了信息融合非线性控制算法的有效性．     

控制单相合金凝固界面形态非线性动力学方程

在非平衡非线性区内，假定在固液界面处为“局域平衡”，考虑了固液界面曲率的非线性、固液界面处温度和成分的非线性，建立单相合金凝固过程中固液界面扰动振幅随时间变化的非线性动力学方程，根据此非线性方程，可以得出在非平衡非线性区内，晶体生长的过程是一非平衡自组织的过程，它可以控制晶体从非稳态到稳定态生长的全过程。     

非平衡凝固理论的发展

近年来,非平衡凝固技术迅速发展,但相应的工艺设计仍主要依赖于经验或半定量理论,无法满足当前材料加工过程控制的苛刻要求.经典凝固理论的建立基于诸多假设,如相图的线性液/固相线假设、合金热力学性质的理想稀溶液假设及体系动力学过程的局域平衡假设,其研究对象为模型合金而非实际浓溶液、多元合金等;经典与现实的差距迫切需要突破经典假设的束缚以更好地发挥凝固理论在工艺设计中的指导作用.本文针对单相固溶体合金,介绍了近年来松弛经典假设后凝固理论的发展.阐述了基于理想稀溶液假设的二元合金微观尺度界面动力学、界面稳定性、枝晶生长,以及耦合微观与宏观的全转变动力学理论的发展,阐明了非线性液/固相线的重要作用.针对二元、多元浓溶液合金,介绍了界面动力学、界面稳定性和枝晶生长理论的发展,阐述了组元间互作用的重要性.当前凝固理论发展摒弃了诸多经典假设,缩短了理论研究与实际工业生产的差距,拓宽了凝固理论的应用范围,更准确地描述了非平衡凝固过程,促进了非平衡凝固理论在工业生产中的应用.     

具有相关失效模式的多自由度非线性结构随机振动系统的可靠性分析

在一般随机有限元法的基础上, 给出了在随机参数的联合概率密度函数未知的情况下, 具有相关失效模式的非线性动态随机结构系统可靠性分析的方法. 应用四阶矩技术、最大熵理论、边缘概率分布函数的概念和不完全概率信息理论, 解决了多自由度非线性随机结构振动系统的首次超限破坏问题.     

一类飞行器姿态动力学特征建模研究

本文研究飞行器姿态动力学的特征建模问题.针对飞行器姿态动力学所具有的三角形式的仿射非线性系统,通过引入非线性系统的时间尺度和一类与系统状态有关的压缩函数,给出了将动力学压缩到特征模型参数中的一般方法,并且给出了特征模型的参数范围及其极限.从所给出的参数范围可以看出,特征模型参数的界与采样周期、建模误差、系统阶数、系统变化率有关.所建立的特征模型的建模误差可以按照控制精度的要求任意小,表明了特征建模和一般模型降阶方法是不同的,该方法并不丢失系统信息.在此基础上建立了挠性卫星姿态特征模型,并给出了参数的界和极限,为基于特征模型的飞行器控制设计奠定了理论基础.     

基于拉格朗日系统中约束力思想的编队卫星构形非线性控制方法

借鉴经典动力学中约束力的思想,提出了一种编队卫星构形精确保持的非线性控制方法．该方法首先将非线性和摄动条件下编队卫星构形保持问题转换为带有完整约束的拉格朗日动力学系统,然后将问题转换为一组微分代数方程,通过求解微分代数方程,确定编队卫星构形保持的非线性控制律．由于借鉴了约束力的思想,该方法自然地利用了编队卫星动力学系统的力学特性,具有节省能量和高精度的特点．通过对线性和非线性条件下空间圆编队卫星构形保持问题的仿真,验证了提出的非线性控制方法的这些特性．     

一个渐近最优的非参数自适应控制器

对一类具有未知非参数非线性结构的离散时间随机控制系统，利用核估计方法和载尾的必然等价原则，设计了一个非参数自适应控制器，并且证明了在不必人为地引进外部激励的情形下，收这个控制器所决定的闭环系统不但是全局稳定的而且是渐近最优的。     

描述颗粒凝并动力学的事件驱动常体积法

构建了“异数目权值虚拟颗粒群策略”和“异数目权值虚拟颗粒凝并准则”，在事件驱动MC的框架下描述数目权值不等的虚拟颗粒之间的凝并事件，并发展“常数目方案”和“阶梯式常数目方案”来保持虚拟颗粒数目在凝并动力学过程中保持在一定范围之内，且保持计算区域体积不变．该方法被命名为事件驱动常体积（EDCV）法．通过与多种主流MC的定量比较，证明EDCV法达到甚至超过了其他随机方法所表现的计算精度和计算效率．     

Stochastic stability of quasi-integrable Hamiltonian systems with time delay by using Lyapunov function method

The asymptotic Lyapunov stability of one quasi-integrable Hamiltonian system with time-delayed feedback control is studied by using Lyapunov functions and stochastic averaging method. First, a quasi-integrable Hamiltonian system with time-delayed feedback control subjected to Gaussian white noise excitation is approximated by a quasi-integrable Hamiltonian system without time delay. Then, stochastic averaging method for quasi-integrable Hamiltonian system is used to reduce the dimension of the original syst...     

p-moment stability of stochastic impulsive differential equations and its application in impulsive control

The exponential p-moment stability of stochastic impulsive differential equations is addressed.A new theorem to ensure the p-moment stability is established for the trivial solution of the stochastic impulsive differential system.As an application of the theorem proposed,the problem of controlling chaos of Lorenz system which is excited by parameter white-noise excitation is considered using impulsive control method.Finally,numerical simulation results are given to verify the feasibility of our approach.     

The logic of experimental tests,particularly of Everettian quantum theory

Claims that the standard procedure for testing scientific theories is inapplicable to Everettian quantum theory, and hence that the theory is untestable, are due to misconceptions about probability and about the logic of experimental testing. Refuting those claims by correcting those misconceptions leads to an improved theory of scientific methodology (based on Popper׳s) and testing, which allows various simplifications, notably the elimination of everything probabilistic from the methodology (‘Bayesian’ credences) and from fundamental physics (stochastic processes).     

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

In this article we propose an extension of singular spectrum analysis for interval-valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set-valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.     

Gamma stochastic volatility models

This paper presents gamma stochastic volatility models and investigates its distributional and time series properties. The parameter estimators obtained by the method of moments are shown analytically to be consistent and asymptotically normal. The simulation results indicate that the estimators behave well. The in‐sample analysis shows that return models with gamma autoregressive stochastic volatility processes capture the leptokurtic nature of return distributions and the slowly decaying autocorrelation functions of squared stock index returns for the USA and UK. In comparison with GARCH and EGARCH models, the gamma autoregressive model picks up the persistence in volatility for the US and UK index returns but not the volatility persistence for the Canadian and Japanese index returns. The out‐of‐sample analysis indicates that the gamma autoregressive model has a superior volatility forecasting performance compared to GARCH and EGARCH models. Copyright © 2006 John Wiley _ Sons, Ltd.     

有色噪声下随机时滞传染病模型的稳定性

本文运用O-U过程刻画环境变化性，在Edoardo Beretta基础上构造了有色噪声影响下的随机时滞的传染病模型。运用一般Lyapunonv方法研究了有色噪声对该系统的影响并得到系统正平衡点保持稳定的充分条件。最后通过对比发现随机扰动对系统稳定性影响仅仅与其随机过程的方差有关。     

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.     

Time-resolved luminescence of perturbed biosystems: Stochastic models and perturbation measures

Measures of biosystem perturbation using a cybernetic approach based on stochastic models of photon emission processes are presented, and compared with classical measures. Perturbation phenomena reflected in non-stationary emission processes are represented by means of filtering theory.     

Forecasting in the presence of level shifts

This article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out‐of‐sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out‐of‐sample inflation forecasting experiment. Copyright © 2005 John Wiley & Sons, Ltd.