涡度方程的时空客观分解与副热带高压的突变与分岔

为了分析研究副热带高压异常活动的动力学机理,基于热力强迫和涡动耗散效应的大气偏微涡度方程,采用Galerkin方法进行方程的时-空变量分离,针对常规方法在空间基函数选择中存在的主观人为性,提出从实际资料场序列中用经验正交函数(EOF)分解与遗传算法结合客观反演空间基函数的思想.选择一组三角函数族作为广义空间基函数,以该基函数与EOF典型场的误差最小二乘和基函数间的完备正交性构造双约束泛函,再引入遗传算法进行空间基函数曲面拟合和系数优化,反演得到客观合理的副热带高压常微动力模型.最后,基于所得非线性动力学模型,对热力强迫作用下的副热带高压的动力学行为和机理进行了分析和讨论,发现太阳辐射加热和纬向海陆热力差异是影响副热带地区位势和流场变化从而导致副高强弱变化和中期进退活动的重要因素,前者以渐变为主;后者则更多表现出突变的特性.通过分析,得到了一些有意义的结果.     

模糊神经网络方法在定点定量降水预报中的应用研究

以单站24小时降水量作为预报对象,采用模糊神经网络方法进行了新的数值预报产品释用预报方法研究.首先通过对T 213、ECMW F预报因子场以及高空气象探测资料进行处理,有效浓缩多种物理量因子场的实况及预报信息,并进一步建立了南宁、桂林、河池、百色4站的降水模糊神经网络释用预报模型.运用与实际业务相同的预报方法对2006年6—8月进行逐日的降水量预报试验,并与相同时次的T 213降水预报产品进行对比分析.结果表明,4个单站的定点、定量模糊神经网络降水预报模型,在预报性能上明显优于同期T 213数值预报模式的降水预报结果.     

偏最小二乘回归方法(PLS)在短期气候预测中的应用

对广西88个站冬季(12月、1月和2月)各月平均气温距平场作自然正交展开(EOF分解),选取累积方差贡献超过90%的前3个主成分作为预报量.从前期平均大气环流场和海温场中查找预报因子,对这些初选因子用偏最小二乘回归方法(PLS)进行信息筛选和成分提取,用提取的新综合变量(又称成分)作预报因子,分别建立各月平均气温前3个主成分的回归预报方程.经独立样本预报试验证明,偏最小二乘回归方法具备良好的因子信息提取能力,其预报建模方法对冬季月平均气温预报具有较好的预测效果.     

一种新的昆虫神经网络预测预报方法

昆虫预测预报是植物保护中的一个困难问题．本文首先从系统的观点出发，分析了影响昆虫种群密度的主要因素；在研究传统时间序列模型共性的基础上，给出了广义时间序列模型；提出了一种新的昆虫预测模式—神经网络预测预报方法；用此方法对棉田害虫烟蓟马进行了预报，数值结果表明这是一种很好的预测预报方法，预测精度较其它方法都有很大提高．     

基于小波变换和改进萤火虫算法优化极限学习机的短期负荷预测

提出了一种基于小波变换和改进萤火虫优化极限学习机的短期负荷预测方法.通过小波分解和重构,对原始负荷序列进行降噪;在模型训练阶段利用改进的萤火虫算法优化极限学习机参数,获得各序列的最优模型;针对各子序列分别预测叠加得到最终预测值.通过在两种时间尺度的数据序列上进行数值计算,与传统的ARMA、BP神经网络、支持向量机及LSSVM等多种经典预测模型相比,模型预测效果更优.     

基于粒子群-支持向量机定量降水集合预报方法

首先对ECMWF不同物理量场预报因子群进行自然正交展开,选取能充分反映每个预报因子场主要信息的第一主分量作为模型输入.进一步利用粒子群算法对支持向量回归机的相关参数进行优化,以南宁市8个气象站单站逐日降水作为预报对象,建立粒子群-支持向量回归集合预报模型,进行单站逐日降水的数值预报产品释用预报方法研究.利用模型对2015年5-6月南宁市8站进行了逐日降水预报业务试验,结果表明,模型具有较好的预报效果.并提出了利用隶属函数建立可信度函数对不同的预报模型进行评价.     

云导风的初步探究

针对云导风反演算法进行了深入研究.通过分析卫星云图的灰度特性和纹理特征,提出了基于最大灰度和梯度交叉相关系数的云导风反演方法.依据最大信息熵准则自适应调整窗口大小,根据NCEP数值预报产品指定风矢高度.实验结果表明,方法有效提高了反演的义精度和效率.     

最优组合预测的短期气候预报建模研究

以预报量序列建立均生函数短期气候预报模型及根据500hPa月平均高度场预报因子分别建立的BP网络模型、回归预报模型为基础,用"误差绝对值和最小"作为最优准则,建立月平均降水量的短期气候组合预测模型.采用线性规划方法计算得到组合预测模型的各权系数,对这种短期气候组合预测模型的预报能力进行了分析研究,结果表明,该组合预测模型的预报精度优于各子方法,具有很好的应用价值.     

基于网络搜索信息的多模态数据驱动航空客流集成预测

为了对机场旅客吞吐量进行更高精度的预测，提出了一种基于网络搜索信息的“分解-重构-集成”组合预测新方法。首先，采用平均影响值和时差相关分析法对机场旅客吞吐量相关的网络搜索关键词进行筛选，合成综合搜索指数。其次，利用改进的自适应白噪声完备集合经验模态分解(ICEEMDAN)方法分别将机场旅客吞吐量和综合搜索指数分解为若干子模态序列，依据子序列的样本熵值重构为高、中、低频序列。以搜索指数中的不同频率成分作为辅助输入信息，分别对机场旅客吞吐量的高频和中频序列采用麻雀搜索算法优化的BP神经网络(SSA-BP)模型进行预测，而低频序列采用自回归分布滞后模型进行预测，最后将不同频率序列预测值用SSA-BP模型进行综合集成得到最终的预测值。通过实证发现，该组合预测新方法能显著提高预测的精度，并表现出较好的鲁棒性。     

涡流检测中识别缺陷形状的一种新方法

涡流检测反演技术是一种非常重要的反演缺陷形状尺寸的无损检测方法．运用Dirichlet边界条件下涡流检测反演的远场区域导数,构造了反演缺陷形状的一种新算法,并且给出了二维及三维的算例,数值反演的结果与实际缺陷吻合得较好．从而说明了:对较小的波数,即使用较少的入射和观测方向的远场测量信息,亦可得到未知缺陷形状的一个合理的重构,算法是可行的、正确的．     

Bay of Bengal wave forecast based on genetic algorithm: A comparison of univariate and multivariate approaches

Prediction of significant wave height (SWH) field is carried out in the Bay of Bengal (BOB) using a combination of empirical orthogonal function (EOF) analysis and genetic algorithm (GA). EOF analysis is performed on 4 years (2005–2008) of numerical wave model generated SWH field, and analyzed fields of zonal (U) and meridional (V) winds. This is to decompose the space-time distributed data into spatial modes ranked by their temporal variances. Two different variants of GA are tested. In the first one, univariate GA is applied to the time series of the first principal component (PC) of SWH in the training dataset after a filtering with singular spectrum analysis (SSA) for effecting noise reduction. The generated equations are used to carry out forecast of SWH field with various lead times. In the second method, multivariate GA is applied to the SSA filtered time series of the first PC of SWH, and time- lagged first PCs of U and V and again forecast equations are generated. Once again the forecast of SWH is carried out with same lead times. The quality of forecast is evaluated in terms of root mean square error of forecast. The results are also compared with buoy data at a location. It is concluded that the method can serve as a cost-effective alternate prediction technique in the BOB.     

多层次统计-动力建模及其应用

该文将子波变换技术和平衡态动力理论结合起来，提出了以气候突变点数为核心的代层次气候建模技术。该技术不仅具有纯粹的动力学意义，而且模式的物理意义十分清楚 ，计算简单。     

粘弹性Timoshenko梁的动力学普遍方程及其动态特性分析

本文用直接力法在时域内推导了粘弹性Timoshenko梁的控制微分方程,它同时计及了材料的拉伸粘性和剪切粘性.为了测定标准线性固体的复模量和三参数,对有机玻璃(PMMA)和尼龙6(PCL)试件成功地应用了强迫振动梁技术.通过大量数值计算,对粘弹性Timoshenko梁的动力特性,特别是阻尼特性进行了分析.结果表明,材料粘性对结构的动力特性,尤其是对阻尼有较大影响。对于高粘性材料,其动力学性质用标准线性固体模型来描写是合适的.     

Long-Term Forecasts of Optimal Approximations to the Load Duration Curve

Little has been done by way of developing an objective technique for long-term forecasting of a utility load duration curve. This paper endeavours to rectify this situation by developing a methodology to provide forecasts of an economically optimal approximation to the load duration curve. A dynamic programming algorithm serves as the basis of the optimal approximations over a known horizon. These approximations are then forecast using time series analysis and an econometric model. The approach is implemented and the results are encouraging.     

Expectation-Stock Dynamics in Multi-Agent Fisheries

In this paper we consider a game-theoretic dynamic model describing the exploitation of a renewable resource. Our model is based on a Cournot oligopoly game where n profit-maximizing players harvest fish and sell their catch on m markets. We assume that the players do not know the law governing the reproduction of the resource. Instead they use an adaptive updating scheme to forecast the future fish stock. We analyze the resulting dynamical system which describes how the fish population and the forecasts (expectations) of the players evolve over time. We provide results on the existence and local stability of steady states. We consider the set of initial conditions which give non-negative trajectories converging to an equilibrium and illustrate how this set can be characterized. We show how such sets may change as some structural parameters of our model are varied and how these changes can be explained. This paper extends existing results in the literature by showing that they also hold in our two-dimensional framework. Moreover, by using analytical and numerical methods, we provide some new results on global dynamics which show that such sets of initial conditions can have complicated topological structures, a situation which may be particularly troublesome for policymakers.     

基于ARIMA-GM组合模型的邮电业务总量预测

对传统预测具有波动性及季节性双重趋势时间序列的模型—ARIMA乘积季节模型进行了改进,先用ARIMA乘积季节模型对邮电业务总量历史数据进行识别和拟合,然后用GM(1,1)模型对其带阀值的残差序列进行修正,最后结合二者得到ARIMA-GM这一组合预测模型.利用此模型对09年上半年中国邮电业务总量进行了预测,结果表明,组合预测方法比单项ARIMA乘积季节模型预测具有更高的精度.     

Insufficiency of chemical network model integration using a high-order Taylor series method

The rate of change for the concentrations of chemical substances in a set of reactions is modeled by a nonlinear dynamical system, which warrants the use of numerical integration methods for differential equations. Previous work advocates the use of a specialized high-order Taylor series method because of an observed reduction in computation time. Contrastingly, we show combinatorial and computational difficulties of the standard Taylor series method, which may dramatically increase computational time or reduce the quality of output. We provide two implementations, a naïve algorithm and an algorithm employing dynamic programming; we are able to overcome only some numerical obstacles and therefore conclude that the Taylor series approach is insufficient for large sets of reactions having many chemical substances.     

Potential inventory cost reductions using advanced time series forecasting techniques

This paper compares demand forecasts computed using the time series forecasting techniques of vector autoregression (VAR) and Bayesian VAR (BVAR) with forecasts computed using exponential smoothing and seasonal decomposition. These forecasts for three demand data series were used to determine three inventory management policies for each time series. The inventory costs associated with each of these policies were used as a further basis for comparison of the forecasting techniques. The results show that the BVAR technique, which uses mixed estimation, is particularly useful in reducing inventory costs in cases where the limited historical data offer little useful information for forecasting. The BVAR technique was effective in improving forecast accuracy and reducing inventory costs in two of the three cases tested. In the third case, unrestricted VAR and exponential smoothing produced the lowest experimental forecast errors and computed inventory costs. Furthermore, this research illustrates that improvements in demand forecasting can provide better cost reductions than relying on stochastic inventory models to provide cost reductions.     

车路耦合非线性振动高阶Galerkin截断研究

将移动车辆模型化为运动的两自由度质量-弹簧-阻尼系统,道路模型化为立方非线性黏弹性地基上的弹性梁,并将路面不平度设定为简谐函数．通过受力分析,建立车路非线性耦合振动高阶偏微分方程．采用高阶Galerkin截断结合数值方法求解耦合系统的动态响应．首次研究不同截断阶数对车路耦合非线性振动动态响应的影响,确定Galerkin截断研究车路耦合振动的收敛性．研究结果表明,对于软土地基的沥青路面,耦合振动的动态响应,需要150阶以上的截断才能达到收敛效果．并通过高阶收敛的Galerkin截断研究了系统参数对车路耦合非线性振动动态响应的影响.     

基于混合密度网络模型拟合收益统计分布与计算Expected Shortfall

基于混合密度网络模型估计金融时间序列的时变条件密件,提出数值模拟方法计算ExpectedShortfall。对香港恒生指数的实证研究表明,混合密度网络可以有效地描述收益的经验分布统计特征和波动规律,模型评估指标反映出预测效果良好,Value-at-R isk的预测精度在高端分位点表现较好,且可有效计算Expected shortfall指标,是金融市场风险测量的有效方法。