基于遗传算法的非高斯系统随机分布控制

传统的控制理论并未考虑工业过程中的一些不确定因素,而这些不确定因素在生产过程中对系统的能源损耗和精度都有很大的影响.为了解决上述问题,本文研究一种基于数据的非高斯随机分布系统优化控制策略,该策略采用核密度估计（KDE）方法完全基于输出数据估计输出概率密度函数（PDF）,根据控制目标建立性能指标函数,采用遗传算法优化性能指标函数,实现输出PDF对目标PDF的跟踪.以磨矿系统为模型进行仿真,采用PDF表征粒度分布（PSD）.仿真结果表明,基于遗传算法的非高斯随机分布最优控制算法能有效地实现随机分布控制系统的控制目标,为实际工业生产提供参考.     

全球大气风速分布与等压面风速分布的关系

一、引言文献从统计力学的观点出发,由实测资料计算分析得出了北半球各等压面上风速分布为n=2的Γ分布.即风速出现于(v,v+Δv)之间者占的相对面积由n=2的Γ概率密度函数决定,其分布函数式为     

热带海洋对大气进行非绝热加热的随机效应的进一步研究

本文考虑了热带海洋对大气进行非绝热加热过程中海温起伏的随机效应,建立了朗之万方程形式的描写大气热量变化的发展方程。由该随机微分方程的定态解,给出了加热势函数和概率密度函数。在确定的热力学参数下,利用实际海温起伏方差值,计算求得了加热势函数的分布及概率密度分布曲线。文中还进一步计算了概率密度流,得到了在两种稳定状态之间穿透势垒的弛豫时间。计算结果表明,海温异常的随机效应是一个长周期过程,时间尺度约为140天,可以用实际测得的拖曳系数C_D和海温起伏方差q2值对它进行预测。      

响应变量随机缺失下部分线性单指标模型的非参数判别检验

研究了响应变量随机缺失情况下部分线性单指标模型的非参数部分检验问题,检验非参数部分预测变量同响应变量之间是否存在非线性关系.用参数和非参数函数的借补估计对缺失响应变量进行插值,并基于借补估计构造了广义似然比检验统计量,证明了其渐近分布性质.     

计算非均匀地表通量的一种简化PDF及其应用

提出描述地表非均匀特性的简化概率密度函数（PDF），它可代替各种具体的PDF用于求解次网格尺度平均通量而不影响其精度，这种简化PDF可直接加入陆面过程模式方程组中进行陆－气通量交换的数值试验。它对非均匀地表陆面过程参数化具有一定的普适性。本文仅讨论了对称分布的简化计算（非对称分布另文讨论），列举了非均匀分布的观测事实，并以地表净红外辐射通量计算为例，详细验证了应用这种简化PDF的可行性和可靠性。     

类多变量方程误差类系统的递阶多新息辨识方法

根据递阶辨识原理,研究了类多变量方程误差系统和类多变量方程误差ARMA系统递阶随机梯度方法和递阶梯度迭代方法、递阶最小二乘方法和递阶最小二乘迭代方法.进一步利用多新息辨识理论,推导了递阶多新息梯度辨识方法和递阶多新息最小二乘辨识方法.为减小计算量,推导了基于滤波的类多变量方程误差ARMA系统递阶辨识方法和递阶多新息辨识方法.讨论了几个典型辨识算法的计算量,并给出了计算参数估计的步骤.     

贵州旱涝灾害监测指标及其应用

利用分布于贵州省84个气象台站1971--2008年的降水资料,计算分析标准化降水指数（SPI）,并对比参考降水距平百分率。结果表明,SPI计算简单,资料容易获取,而且计算结果与降水距平百分率有极好的一致性。同时,由于SPI是通过概率密度函数求解累积概率,再将累积概率标准化而得,具有稳定的计算特性,消除了降水的时空分布差异,具有客观、定量的优点。因此,在各个区域和各个时段均能有效地反映旱涝状况,能较好的运用于贵州地区的干旱监测业务。     

基于多模式集合方案的中国东部夏季降水概率季度预测

借助ENSEMBLES计划提供的5个海-气耦合模式(CGCM)的多初值后报降水资料,采用常用的4种多模式集合方案,即等权集合(EE)、对单个集合成员先订正再等权集合(Cali-EE)、基于多元线性回归的集合方案(MLR)、基于贝叶斯统计学的集合方案(Bayes),制作1960—2005年中国东部夏季降水概率密度函数(PDF)季度预测。在此基础上,比较最优(技巧最高)集合方案与气候学预测(衡量概率密度函数预测是否有技巧的基准)的技巧,初步评估目前基于多模式集合方案的、中国东部夏季降水的概率密度函数季度预测能力。结果表明,Bayes方案在华南最优,Cali-EE在长江流域、江淮流域以及中国北方的中部最优,MLR在中国北方的东部最优;基于这些最优集合方案的概率密度函数预测产品均具有高校准度,且其锐度高于或接近气候学预测;并且,对于所有区域,最优集合方案的预测技巧总是高于气候学预测,这暗示即使不提取模式其他变量中所包含的预测信息,对于中国东部夏季降水季度预测,常用的多模式集合方案也已具备制作有技巧的概率密度函数预测产品的能力。     

Copula函数在广西洪涝灾害的降水概率预测中的应用

为了研究洪涝的重要致灾源因子降水的概率,利用广西从1970年-2012年的降水数据,以年降水均值（X）和年降水极值均值（Y）为研究变量,通过Copula函数构建其联合分布.经过OLS,AIC拟合优度评价,采用Copula函数中拟合效果较好的Gumbel-Hougaard Copula函数建立边缘分布为Pearson-Ⅲ型的两变量的联合分布.随后,进行相应的重现期计算,计算结果表明,在联合重现期下的两变量降水设计值比单变量设计值和同现重现期下的设计值都要高,故采用联合重现期下的联合设计值作为防洪标准会更加安全.最后,对比降水数据与灾情数据,研究降水重现期与洪涝灾情的关系.结果表明,降水的重现期越长,洪涝灾害也越严重.     

一模式大气参考状态的计算与特征

在实现计算T106L19模式大气参考状态的基础上,给出了两套有限区域模式大气参考状态,并对其数理特征进行了研究。讨论了可供计算的大气参考状态模式和实现了具体的模式大气参考状态。结果表明,有限区域大气参考状态其空气熵比较符合数学Γ概率密度函数分布,且其低层总存在一个逆温层,但都随季节及大气环流本身有着规律变化。     

ON THE STOCHASTIC ANALYSIS OF A SIMPLE CLIMATE MODEL

By using the first thermodynamic law and stochastic function,a stochastic nonlinear climate model isproposed and the model analytical solution is obtained.The result indicates that the global temperature prob-ability density function has two peaks.If the solar radiation varies,a sudden change in the number of peakwill happen.In addition,we find that there are different sea temperature responses at different latitudes.     

简单气候模式的随机分析解

本文根据热力学第一定律和随机函数建立了一个随机、非线性气候模式,并求出了模式的解析解。结果表明:模式的温度概率密度是一条有双峰值的曲线,太阳辐射的改变将使曲线上峰的数目发生突变;另外,在利用模式分析海温时还发现:各纬度海温对天气过程的扰动作用将呈现不同的响应。     

随机马尔可夫跳变系统的弹性动态输出反馈控制

本文讨论了随机噪声影响下马尔可夫跳变系统的弹性动态输出反馈控制问题.在系统随机干扰和控制输入扰动的情况下,设计的弹性控制器可以确保闭环系统的依概率渐近稳定性.通过运用随机微分方程理论和线性矩阵不等式技术对系统进行稳定性分析,获得了系统依概率渐近稳定的充分条件和控制器增益.最后通过数值算例和直升机系统仿真验证了所提弹性动态输出反馈控制方法的有效性.     

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

We address the inverse problem of source reconstruction for the difficult case of multiple sources when the number of sources is unknown a priori. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release time and duration) that characterize each source. A mapping (source–receptor relationship) that relates a multiple source distribution to the concentration measurements made by an array of detectors is formulated based on a forward-time Lagrangian stochastic model. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source–receptor relationship and realized in terms of a backward-time Lagrangian stochastic model, is described. An efficient computational algorithm based on a parallel tempered Metropolis-coupled reversible-jump Markov chain Monte Carlo (MCMC) method is formulated and implemented to draw samples from the posterior probability density function of the source parameters. This methodology allows the MCMC method to initiate jumps between the hypothesis spaces corresponding to different numbers of sources in the source distribution and, thereby, allows a sample from the full joint posterior distribution of the number of sources and the parameters for each source to be obtained. The proposed methodology for source reconstruction is tested using synthetic concentration data generated for cases involving two and three unknown sources.     

A nonlinear stochastic low-order energy balance climate model

The effects of stochastic forcing on a one-dimensional, energy balance climate model are considered. A linear, stochastic model is reviewed in analogy with the Brownian motion problem from classical statistical mechanics. An analogous nonlinear model is studied and shows different behavior from the linear model. The source of the nonlinearity is the dynamical heat transport. The role of nonlinearity in coupling different temporal and spatial scales of the atmosphere is examined. The Fokker-Planck equation from statistical mechanics is used to obtain a time evolution equation for the probability density function for the climate, and the climatic potential function is calculated. Analytical solutions to the steady-state Fokker-Planck equation are obtained, while the time-dependent solution is obtained numerically. The spread of the energy produced by a stochastic forcing element is found to be characterized by movement mainly from smaller to larger scales. Forced and free variations of climate are also explicitly considered.     

STOCHASTIC SIMULATION OF TROPICAL CYCLONE TRACKS IN THE NORTHWEST PACIFIC REGION WITH CLASSIFICATION MODEL

Accurate simulation of tropical cyclone tracks is a prerequisite for tropical cyclone risk assessment. Against the spatial characteristics of tropical cyclone tracks in the Northwest Pacific region, stochastic simulation method based on classification model is used to simulate tropical cyclone tracks in this region. Such simulation includes the classification method, the genesis model, the traveling model, and the lysis model. Tropical cyclone tracks in the Northwest Pacific region are classified into five categories on the basis of its movement characteristics and steering positions. In the genesis model, Gaussian kernel probability density functions with the biased cross validation method are used to simulate the annual occurrence number and genesis positions. The traveling model is established on the basis of the mean and mean square error of the historical 6 h latitude and longitude displacements. The termination probability is used as the discrimination standard in the lysis model. Then, this stochastic simulation method of tropical cyclone tracks is applied and qualitatively evaluated with different diagnostics. Results show that the tropical cyclone tracks in Northwest Pacific can be satisfactorily simulated with this classification model.     

Lagrangian Stochastic Models For Turbulent Dispersion In The Atmospheric Boundary Layer

A one-particle three-dimensional stochastic Lagrangian model fortransport of particles in a horizontally-homogeneous atmosphericsurface layer with arbitrary one-point probability density functionof Eulerian velocity fluctuations is suggested. A uniquely definedLagrangian stochastic model in the class of well-mixed models isconstructed from physically plausible assumptions. These assumptionsare: (i) in the neutrally stratified horizontally homogeneous surface layer, the vertical motion is mainly controlled by eddies whose size is of order of the current height; and (ii), the streamwise drift term is independent of the crosswind velocity. Numerical simulations for neutral stratification have shown a good agreement of our model with the well-known Thomson's model, with Flesch and Wilson's model, and with experimental measurements as well. However there is a discrepancy of these results with the results obtained by Reynolds' model.     

Dispersion in sheared Gaussian homogeneous turbulence

By integrating the Fokker-Planck equation corresponding to a Lagrangian stochastic trajectory model, which is consitent with the selection criterion of Thomson (1987), an analytical solution is given for the joint probability density functionp(x_i, u_i, t) for the position (x _i) and velocity (u _i) at timet of a neutral particle released into linearly-sheared, homogeneous turbulence. The solution is compared with dispersion experiments conforming to the restrictions of the model and with a shortrange experiment performed in highly inhomogeneous turbulence within and above a model crop canopy. When the turbulence intensity, wind shear and covariance are strong, the present solution is better than simpler solutions (Taylor, 1921; Durbin, 1983) and as good as any numerical Lagrangian stochastic model yet reported.