城市货运发展统计数据分析与建模

21世纪的物流在上海的经济活动中扮演着重要的角色,货运在城市交通中所占的比重日益增 加,如何科学地预测货运总量的发展趋势,对上海城市交通规划有着重要的意义。本文采用基于时间序列的 ARIMA模型,构建了上海货运总量发展模型,并对上海中短期的货运发展情况进行预测。     

随机模糊神经网络模型预测铁水硅含量

该文在考虑了具有模糊化和非模糊化的模糊逻辑系统用于高炉铁水含硅量[Si]时各种噪声干扰的同时,把模糊数学理论和随机系统理论结合在一起,建立了一种新的高炉铁水含硅量[Si]的智能预报模型,该模型是由非单值模糊化、模糊规则库、模糊推理机、特殊非模糊化构成的随机模糊神经网络逻辑系统.通过结合莱钢1号高炉实时在线采集的生产数据,进行仿真研究,结果表明,该套模型能够很好地预测高炉铁水含硅量[Si].     

怎样认识哲学在当代的命运

改革开放以来，人们对哲学及马克思主义哲学的现状和前景已经进行和正在进行着种种思考。经过以解放思想为界的前后对比，对哲学正常功能的分析，本文明确提出在时代大变动之际，特别是基于我国建立社会主义市场经济这一现实，对哲学的需求是非常迫切的，就此展示了马克思主义哲学的地位和命运。     

我国住房抵押贷款证券化的必要性和可行性分析

随着我国房地产业的高速发展和单一的房地产银行融资手段，我国商业银行住房抵押贷款余额占其贷款余额的比重越来越高，从而使得我国商业银行面临的金融风险越来越大，而且单一的银行融资手段不利于我国房地产业的高速发展，住房抵押贷款证券化对我国房地产业和银行业的发展意义巨大。本文试分析了住房抵押贷款证券化这一金融创新工具在我国实行的必要性和可行性。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

战时装备维修备件品种确定步骤与方法研究

着眼于战时特点,阐述准确确定战时装备维修备件需求的重要性,对我军在确定战时装备维修备件需求方面存在的不足进行了分析。针对现代战争“精确化”保障的特殊要求,结合我军现有的理论成果和实践经验,探讨系统科学地确定战时装备维修备件品种的程序和方法。     

区域物流需求量预测分析

区域物流需求量预测是政府有关部门制定物流规划和建设物流基地的决策依据，也是第三方物流企业发展物流业务的重要前提。本文在对区域需求量变化的影响因素分析以及对区域物流需求量指标选取的基础上．采用灰色预测方法对区域物流需求量进行预测。     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.     

基于时间序列模型在物流需求预测中的应用

本文采用时间序列模型进行物流需求预测，用实例从数据平稳性的判断，平稳化、标准化，建模，经模型的识别、定价、参数估计和检验，到预测及误差和置信区间的计算，详细地说明了时间序列模型在物流需求预测中是如何应用的。结果表明该ARMA模型能够较好地拟合并可获得较高的中短期预测精度。     

灰色预测算法在物流园区需求预测中的应用

对物流园区需求预测进行了系统分析,介绍了灰色预测算法,和物流园区腹地划分方法;结合连云港港口物流园区需求预测的实例进一步阐述了灰色预测算法在物流园区需求预测中的应用.