网络延迟对基于高层体系结构/运行时支撑构件的分布交互仿真系统的影响

研究了网络延迟对基于HLA／RTI的分布交互式仿真系统的影响。给出了分布式仿真实验环境的细节，提供了网络仿真的工具，模拟了50ms，100ms，200ms的网络延迟，研究了网络延迟对仿真进程实时性的影响。采用状态数据的时戳技术和状态预测算法，可以降低网络传输时间的影响，实验表明在200ms的延迟下实体定位的偏差可以降到0．5m以下。     

基于灰色系统模型预测高校办学效益

对高校办学效益评估预测是高校战略管理的一项重要内容。灰色系统模型是高校办学效益预测的重要工具,它将发挥越来越重要的作用。本文着重介绍灰色系统模型及其在高校办学效益中的应用,并进行实例验证,以探索一条新的办学效益评估预测方法。     

Dynamic learning rates algorithm for BPNN to forecast time series of dam security

Time series data of dam security have a large number of observed values and should be forecasted accurately in time. Neural networks have the powerful approach ablilities of arbitrary functions and have been broadly utilized in many domains. In this paper, a dynamic learning rate training algorithm of back-propagation neural networks for time series forecasting is proposed and the networks with this algorithm are built to forecast time series of dam security. The application results demonostrate the efficiency of modelling and the effictiveness of forecasting.     

Seasonal prediction of European cereal prices: good forecasts using bad models?

Because of their natural adherence to the climate and pronounced seasonal cycles, prices of field crops constitute an interesting field for exploring seasonal time series models. We consider quarterly prices of two major cereals: barley and wheat. Using traditional in‐sample fit and moving‐window techniques, we investigate whether seasonality is deterministic or unit‐root stochastic and whether seasonal cycles have converged over time. We find that seasonal cycles in the data are mainly deterministic and that evidence on common cycles across countries differs for the two commodities. Out‐of‐sample prediction experiments, however, yield a ranking with respect to accuracy that does not match the statistical in‐sample evidence. Parametric bootstrap experiments establish that the observed mismatch is indeed an inherent and systematic feature. Copyright © 2008 John Wiley & Sons, Ltd.     

洪水涨落水位-流量分布规律及应用

以河溶水文站为例,采用系统分析法研究受洪水涨落影响绳套关系水位-流量关系轴线摆动变化范围。结果表明:受洪水涨落影响绳套关系水位-流量洪水洪峰水位及相应流量的轴线摆动有一定规律,水位-流量关系走向越接近洪峰水位越向轴线形成有规律的系列,由此可根据洪峰水位与相应流量之间建立回归关系,建立绳套关系水位-流量转换模型应用于洪峰水位预报,有效解决受洪水涨落影响下绳套关系水位-流量关系洪水水位预报瓶颈,满足防汛需求。     

基于IOWC-GOWA算子及相对熵的区间型组合预测模型

针对用区间型数据描述不确定现象的组合预测问题,为了提高区间型数据的预测精度,首先采用诱导有序加权连续区间的广义有序加权平均(IOWC-GOWA)算子将区间数集结为实数;然后对集结后的实数进行标准化处理;最后从信息论的角度引入相对熵作为最优准则,提出了基于IOWC-GOWA算子及相对熵的区间型组合预测模型;另外,通过实例分析了该组合预测模型的合理性和有效性;结果表明:该组合预测模型可以有效地提高区间型数据的预测精度,即该模型是合理有效的,并且,参数λ和BUM函数的选取会对模型的预测精度产生一定的影响。     

基于综合权重因子的城市时需水量预测

提出了基于综合权重因子的城市时需水量预测方法。根据预测日前几日和前几周的用水量数据,计算平均时用水量;引入综合权重因子,构建预测日前1日时用水量模型。结合日需水量预测数据,将获得的最优连续日数、最优连续周数和综合权重因子,用于预测日的时需水量预测。算例验证结果表明,预测值平均绝对百分比误差较小。     

注意力机制下的EMD-GRU短期电力负荷预测

为进一步提高短期电力负荷预测精度,构建一种基于注意力机制的经验模态分解(EMD)和门控循环单元(GRU)混合模型,对时间序列的短期负荷进行预测.首先,对负荷序列进行EMD,将数据重构成多个分量;再通过GRU提取各分量中时序数据的潜藏特征;经注意力机制突出关键特征后,分别对各分量进行预测;最后,将各分量的预测结果叠加,得到最终预测值.仿真结果表明:相对于BP网络模型、支持向量机(SVR)模型、GRU网络模型和EMD-GRU模型,基于EMD-GRU-Attention的混合预测模型能取得更高的预测精度,有效地提高短期电力负荷预测精度.     

Combining var estimation and state space model reduction for simple good predictions

A combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.     

高等网络教育市场与价值链研究

分析了我国高等网络教育市场现状、市场管制、市场结构和市场趋势等，介绍了我国高等网络教育价值链的构成和各个子环节及其作用和参与者。