共查询到17条相似文献，搜索用时 93 毫秒
1.
丁锋 《南京气象学院学报》2010,(6):481-488
对于输出误差模型描述的多输入单输出系统,辨识的困难在于辨识模型信息向量中包含系统未知输出量(真实输出或无噪输出),以致标准辨识算法无法应用.提出了利用输出估计代替系统真实输出的辨识思想,即通过估计模型预测(估算)系统输出,利用这个估计输出来递推计算系统参数,进而提出了基于输出估计的随机梯度辨识算法,并研究了算法的收敛性,给出了仿真例子. 相似文献
2.
主要研究线性随机微分方程模型,为此定义Itô随机微积分,建立Itô公式.鉴于研究的重点是利用R软件进行数值模拟,所以详细讨论了过去10多年来随机微分方程数值解的研究. 相似文献
3.
主要研究金融领域中的多种数学模型,重点是利用R软件进行数值模拟.继续讨论更多的随机微分方程(SDE)模型,包括均值回归过程、均值回归的Ornstein-Uhlenbeck(OU)过程、平方根过程、CIR模型以及Θ过程.进一步,将对当前SDE数值解的研究给出更深入的建议. 相似文献
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主要讨论了 Cox-Ross-Rubinstein (CRR) 模型和广义的CRR模型,并研究了如何基于CRR模型和广义的CRR模型利用Monte Carlo模拟计算资产价格以及期权价值. 相似文献
6.
《高原气象》2018,(5)
采用随机森林RF(Random Forest)模型对雅鲁藏布江流域22个站点的日平均气温进行降尺度研究,为了探求在雅鲁藏布江流域更适宜的气温降尺度方法,采用多元线性回归MLR、人工神经网络ANN和支持向量机SVM三种方法作为对比模型,并且采用主成分分析PCA和偏相关分析PAR两种分析方法,进行特征变量筛选。采用纳西效率系数NASH、均方根误差RMSE系数、绝对误差MAE和相关系数r值四种标准来评价模型的模拟效果。结果表明,RF模型的模拟效果要明显优于其他几种方法的模拟结果;采用PAR筛选特征变量的模型计算结果,不仅优于采用PCA筛选特征变量模型的模拟结果,且较稳定,另外,各种模型验证期的NASH效率系数都在0. 86以上,相关系数都在0. 93以上,所用几种模型都能较好地模拟雅江流域平均气温。选取MPI-ESM-LR模式在未来(2016-2050年)两种极端典型浓度路径RCP(Representative Concentration Pathway)排放情景RCP2. 6和RCP8. 5下的试验数据,研究雅鲁藏布江流域未来气温变化趋势表明,雅鲁藏布江流域未来2016-2050年在RCP2. 6和RCP8. 5两种排放情景下,平均气温都呈现出持续上升的趋势,在RCP2. 6排放情景下日平均气温平均上升0. 14℃,在RCP8. 5排放情景下日平均气温平均上升0. 30℃。 相似文献
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研究了负相依索赔条件下带常数利率的风险模型在随机区间上的破产问题.假设随机埋单服从指数分布,通过分析随机时间与有限时间之间的关系,得到了该模型破产概率的渐近表达式. 相似文献
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针对控制器存在随机不确定性的多智能体系统,研究了所有智能体状态达到一致的控制问题.首先,假设智能体连接网络拓扑是无向、固定和连通的,而且每个个体的控制器存在相同的随机不确定性,最终得到了融合随机控制器增益不确定的闭环控制系统模型.应用基于李雅普诺夫稳定性理论、线性矩阵不等式技术和鲁棒控制方法,得到了一种保证误差状态系统渐近稳定的充分条件.进一步通过一系列矩阵变换处理技巧,将状态反馈控制器的存在条件转化为一组线性矩阵不等式的可行解问题.最后应用计算机仿真验证了该控制器设计结果的有效性. 相似文献
9.
主要研究了Black-Scholes模型.与Black-Scholes期权定价公式相比,将再次强调和证实利用R软件Monte Carlo模拟的强大作用. 相似文献
10.
研究了随机滞后微分方程的一致有界和一致最终有界.利用Lyapunov函数和Razumikhin技巧,得到了一些关于随机滞后微分方程有界性新的Razumikhin定理,同时,证明随机滞后微分方程解的存在性,推广了相关的文献.最后,给出例子证实定理的有效性. 相似文献
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The general Ekman momentum approximation boundary-layer model(GEM) can be effectively used to describe the physical processes of the boundary layer. However, eddy viscosity, which is an approximated value, can lead to uncertainty in the solutions. In this paper, stochastic eddy viscosity is taken into consideration in the GEM, and generalized polynomial chaos is used to quantify the uncertainty. The goal of uncertainty quantification is to investigate the effects of uncertainty in the eddy viscosity on the model and to subsequently provide reliable distribution of simulation results. The performances of the stochastic eddy viscosity and generalized polynomial chaos method are validated based on three different types of eddy viscosities, and the results are compared based on the Monte Carlo method. The results indicate that the generalized polynomial chaos method can be accurately and efficiently used in uncertainty quantification for the GEM with stochastic eddy viscosity. 相似文献
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Despite recent advances in supercomputing, current general circulation models poorly represent the variability associated with organized tropical convection. In a recent study, the authors have shown, in the context of a paradigm two baroclinic mode system, that a stochastic multicloud convective parameterization based on three cloud types (congestus, deep and stratiform) can be used to improve the variability and the dynamical structure of tropical convection. Here, the stochastic multicloud model is modified with a lag type stratiform closure and augmented with an explicit mechanism for congestus detrainment moistening. These modifications improve the representation of intermittent coherent structures such as synoptic and mesoscale convective systems. Moreover, the new stratiform-lag closure allows for increased robustness of the coherent features of the model with respect to the amount of stochastic noise and leading to a multi-scale organization of slowly moving waves envelopes in which short-lived and chaotic convective events persist. Congestus cloud decks dominate the suppressed-dry phase of the wave envelopes. The simulations with the new closure have a higher amount of stochastic noise and result in a Walker type circulation with realistic mean and coherent variability which surpasses results of previous deterministic and stochastic multicloud models in the same parameter regime. Further, deterministic mean field limit equations (DMFLE) for the stochastic multicloud model are considered. Aside from providing a link to the deterministic multicloud parameterization, the DMFLE allow a judicious way of determining the amount of deterministic and stochastic “chaos” in the system. It is shown that with the old stratiform heating closure, the stochastic process accounts for most of the chaotic behavior. The simulations with the new stratiform heating closure exhibit a mixture of stochastic and deterministic chaos. The highly chaotic dynamics in the simulations with congestus detrainment mechanism is due to the strongly nonlinear and numerically stiff deterministic dynamics. In the latter two cases, the DMFLE can be viewed as a “standalone” parameterization, which is capable of capturing some dynamical features of the stochastic parameterization. Furthermore, it is shown that, in spatially extended simulations, the stochastic multicloud model can capture qualitatively two local statistical features of the observations: long and short auto-correlation times of moisture and precipitation, respectively and the approximate power-law in the probability density of precipitation event size for large precipitation events. The latter feature is not reproduced in the column simulations. This fact underscores the importance of gravity waves and large scale moisture convergence. 相似文献
13.
The El Niño stochastic oscillator 总被引：1，自引：1，他引：0
G. Burgers 《Climate Dynamics》1999,15(7):521-531
A stochastic model is fitted to the observed NINO3.4 time series between 1951–1995. The model is nothing more than the complex version of a first-order autoregressive process. The autocorrelation of this stochastic oscillator model is an exponentially decaying cosine, specified by three parameters: a period, a decay time, and a phase shift. It fits the observed NINO3.4 autocorrelation quite well. Anomalies during an El Niño can be characterized to a large extent by a single, irregularly oscillating, index. Equatorial wave dynamics and delayed-oscillator models have been used to explain this behaviour, and it has been suggested that El Niño might be a stable phenomenon excited by weather noise. Assuming this is the case, the stochastic oscillator has a direct physical interpretation: the parameters of the oscillation can be linked to dynamical models of the delayed-oscillator type, and the noise terms represent random influences, such as intraseasonal oscillations. Long Monte Carlo simulations with the stochastic oscillator show substantial decadal variability and variation in predictability. The observed decadal variability is comparable, except for the rather large rise in the long-term mean around 1980. The observed seasonal dependence of El Niño behaviour is not compatible with the natural variability of a stationary stochastic oscillator. Formulating the model in terms of standardized anomalies takes into account some of the seasonal dependence. A stochastic oscillator forecast model has a skill approaching that of more comprehensive statistical models and may thus serve as an appropriate baseline for the skill of El Niño forecasting systems. 相似文献
14.
Jun Xia 《Theoretical and Applied Climatology》1996,55(1-4):177-183
Summary A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.With 4 Figures 相似文献
15.
Richard b. Howarth 《Climatic change》2003,56(3):257-263
Monte Carlo simulations by Tol (2003) suggest that there is a small but positive probability that climate change will impose catastrophic impacts on future society unless sufficient steps are taken to reduce greenhouse gas emissions. This paper critically evaluates this finding and its implications for policy analysis and decision-making. 相似文献
16.
Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August. 相似文献
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随机天气模型参数化方案的研究及其模拟能力评估 总被引：8，自引：2，他引：6
文中介绍了随机天气模型 WGEN的基本结构及其模拟原理 ,并针对其中随机过程的统计结构特征和 GCMs输出要素的不同时空尺度特点 ,利用动态数据的参数化分析方法等统计学技术 ,确定了该模型参数的估计方法。同时基于蒙特卡罗数值计算原理 ,给出了 WGEN的随机试验方法 ,并通过模拟基准气候 ,从时间分布和空间场两方面对模型在中国东北地区的模拟效果及其能力进行了评估。结果表明 ,模型对于最高气温、最低气温、降水和辐射等要素均具有较好的模拟效果 ,模拟序列与观测序列的取值分布有较一致的概率特性。由此可以结合 GCMs大尺度网格上输出的月和年要素值 ,通过调控随机过程的参数 ,生成具有不同气候变率的 2× CO2 逐日气候变化情景 ,实现气候预测模式与气候影响模式的嵌套 ,进一步研究气候变率变化的可能影响。 相似文献