高炉炼铁含硅量的动态预测

针对高炉炼铁过程中含硅量预测滞后性和复杂性等问题,建立以改进型Elman神经网络为基础的铁水含硅量动态预测数学模型,利用MATLAB工具箱对动态预测模型进行仿真和预测.首先利用蒙特卡洛法随机选取一组数据作为仿真数据样本,对改进后的Elman神经网络进行离线训练.然后用训练后的Elman神经网络进行铁水含硅量预测,再用残差分析法分析预测值与数据样本之间的显著性差异,用统计检验法讨论模型的可行性.结果表明,铁水含硅量数值预测误差较小,铁水含硅量的实际值和预测值无显著性差异,含硅量的测结果是可信的.     

Estimation in monotone single‐index models

Single‐index models are popular regression models that are more flexible than linear models and still maintain more structure than purely nonparametric models. We consider the problem of estimating the regression parameters under a monotonicity constraint on the unknown link function. In contrast to the standard approach of using smoothing techniques, we review different “non‐smooth” estimators that avoid the difficult smoothing parameter selection. For about 30 years, one has had the conjecture that the profile least squares estimator is an ‐consistent estimator of the regression parameter, but the only non‐smooth argmin/argmax estimators that are actually known to achieve this ‐rate are not based on the nonparametric least squares estimator of the link function. However, solving a score equation corresponding to the least squares approach results in ‐consistent estimators. We illustrate the good behavior of the score approach via simulations. The connection with the binary choice and current status linear regression models is also discussed.     

A Bayesian asymmetric logistic model of factors underlying team success in top‐level basketball in Spain

This paper analyses the factors underlying the victories and defeats of the Spanish basketball teams Real Madrid and Barcelona in the national league, ACB. The following research questions were addressed: (a) Is it possible to identify the factors underlying these results? (b) Can knowledge of these factors increase the probability of winning and thus help coaches take better decisions? We analysed 80 and 79 games played in the 2013–2014 season by Real Madrid and Barcelona, respectively. Logistic regression analysis was performed to predict the probability of the team winning. The models were estimated by standard (frequentist) and Bayesian methods, taking into account the asymmetry of the data, that is, the fact that the database contained many more wins than losses. Thus, the analysis consisted of an asymmetric logistic regression. From the Bayesian standpoint, this model was considered the most appropriate, as it highlighted relevant factors that might remain undetected by standard logistic regression. The prediction quality of the models obtained was tested by application to the results produced in the following season (2014–2015). Again, asymmetric logistic regression achieved the best results. In view of the study findings, we make various practical recommendations to improve decision making in this field. In short, asymmetric logistic regression is a valuable tool that can help coaches improve their game strategies.     

报文抽样测量技术及应用

报文抽样技术是高速网络流量测量和网络QoS管理中使用的一项关键技术。本文从报文抽样的数学理论出发,对数学推导的重要结论进行了讨论和分析;然后在研究了当前各种报文抽样策略、机制及简单性能衡量之后,分析了当前的报文抽样技术发展现状;随后对报文抽样的新进展进行了讨论;最后阐述了报文抽样技术的发展趋势及其在现代网络测量、管理中的应用情况。     

中国沿海地区经济发展态势及发展对策

本文在回顾改革开放以来沿海地区经济发展历史的基础上,就目前经济发展的状况作了详细剖析,分析了发展态势,并利用灰色等维递补模型对到 ２０１０ 年的经济总量、结构的发展趋势进行预测与分析,以此为基础,认为沿海地区在新世纪保持经济持续适速发展的关键在于树立可持续的发展理念、发展高新技术产业为基础的第三产业和建立分工明确、联系紧密的大沿海经济区。     

基于仿真的备件消耗预测方法研究

针对目前装备备件消耗预测遇到的问题,文章提出基于仿真的备件消耗预测方法。首先分析了装备维修保障系统的结构和运行规律,在此基础上建立了面向仿真的装备维修保障模型,包括装备使用任务模型、装备系统模型、维修保障系统模型、使用与维修保障活动模型。借助SIMLOX仿真平台,结合案例想定对上述模型进行了仿真实现,得到备件消耗的品种和数量。     

从货币产出观看提高中国货币政策调控效果的对策

关于货币产出观,西方理论界一直都存在争论.古典学派、理性预期学派、实际经济周期学派等认为货币与真实经济无关,魏克塞尔、凯恩斯学派、新古典综合学派等认为货币变动对产出有重要影响.这些争论无形之中给予我们以启示.由于我国货币政策调控效果不明显,应增强货币政策决策的预见性和科学性,加快经济金融等制度创新和体制改革,规范微观主体行为,注重结构调整并与供给管理政策相结合.     

基于月度数据的景区客流预测优化研究——以黄山风景区为例

采用当前使用极为广泛的9种方法,利用黄山风景区24年（1987年1月—2010年12月）的月度客流数据进行2年时间尺度的预测,并采用均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分误差（MAPE）和希尔不等系数（TIC）等4个预测性能指标对预测模型进行比较优化,发挥旅游客流预报对于景区管理者的决策支持作用。研究结果表明：ARIMA方法在预测性能4个指标中有3个最优,LS-SVM方法的MAPE表现最优。而其他方法在本研究中则未表现出明显的优势。     

基于改进的灰色Markov链的预测方法及其实证

文章将灰色预测模型GM(1,1)与Markov链方法相结合,建立灰色Markov预测模型,预测波动上升的数据系列,改进了状态转移矩阵的传统计算方法,对状态施行动态滑动转移,用更新的一步状态转移概率矩阵,对未来数据所处的状态进行一步或多步预测.在实证中,以黄石市农民年人均纯收入为例,把农民收入看作一个灰色系统,农民人均收入看作灰色量,通过过去年份的数据挖掘,在Mathematica7.0环境下,给出了2009年黄石农民年人均纯收入的状态预测,并提出影响预测精度的若干因素.     

大数据时代高校思想政治教育工作的挑战与对策

随着互联网技术的革新,大数据已延伸至各个行业和领域,为教育工作带来深刻变革.大数据为高校思想政治教育工作带来前瞻性和多样性,改变高校思想政治教育研究方式,丰富和拓展思想政治教育领域.高校思想政治教育工作者需要树立大数据意识,提高思想政治队伍的数据处理能力和水平,逐步完善规章制度和流程,从而彰显大数据技术在高校思想政治教育领域的价值.