一种基于粗糙集理论的SVM短期负荷预测方法

在分析粗糙集理论方法与支持向量机方法的优势和互补性后,探讨了粗糙集与支持向量机的结合方法,提出了一种基于粗糙集数据预处理的支持向量机预测系统。该系统利用粗糙集理论在处理大数据量、消除冗余信息等方面的优势,减少支持向量机的训练数据,克服支持向量机方法因为数据量太大,处理速度慢等缺点。将该系统应用于短期负荷预测中,与BP神经网络法和标准的支持向量机方法相比,得到了较高的预测精度,从而说明了基于粗糙集理论方法作为信息预处理的支持向量机学习系统的优越性。     

基于改进多孔算法的时间序列预测

针对小波分析技术存在的边界问题,提出一种改进的多孔算法。使用该算法得到的系数序列,在具备时移不变性的同时,消除了右侧边界存在数据畸变的现象,使小波分析技术结合神经网络等传统预测模型的方法应用于单变量时间序列预测任务具备可行性。为进一步提高预测精度,引入了神经网络集成技术以改善网络泛化能力。实验表明,这种组合预测模型预测效果与稳定性优于传统预测模型。     

城市交通网络中商业街的选址分析与计算

商业街在城市交通网络体系中的选址是商业街布局的重要方面。本文提出交通网络商业街选址问题并建立数学模型，给出了两种选址原则：（1）城市中所有节点至商业街（只需要达到商业街的其中一个节点）之间的最短路径之和最小。（2）城市中所有点对之间过商业街的最短路径之和最小。针对以上两个方面，分另4给出其计算方法，并进行了算法的时间和空间复杂性分析。     

广义加权多重函数平均组合预测及其参数估计

提出一种新的组合预测模型———广义加权多重函数平均组合预测模型,并利用二次规划算法给出其加权系数的不同参数估计方法。该模型具有广泛的代表性,它集多种组合预测模型于一体,是一种新的群组集结方法,通过选择合适的模型组合形式及最佳的模型参数,能够有效地提高预测精度。预测实例表明了该模型的有效性。     

基于区间数距离的区间组合预测模型

针对预测值为区间数形式,引入区间数距离,提出一种确定区间组合预测权系数的方法.给出两个区间序列的距离公式,以实际观测序列与组合预测序列的距离最小为准则,建立规划模型,求解得到权系数.最后,通过实例,证明模型能够有效提高预测的精度.     

基于ESI和InCites的学科发展预测研究--以中国地质大学为例

基于汤姆路透公司的ESI和InCites数据库,分析了中国地质大学各学科的特征,探讨了高被引论文、热门论文、顶尖论文与学科发展的关系,并预测后续最有可能入围ESI世界前1％的学科。     

Combining multiple forecasts given multiple objectives

This paper is concerned with expanding the decision support capabilities of computerized forecasting systems. The expansion allows for the systematic combination of multiple forecasts and the explicit consideration of multiple objectives in the forecast selection process. The methodology used is multiple objective linear programming. Selecting an individual forecast based upon a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. Even if an individual forecast does provide a good fit with respect to a single objective, a combined forecast may provide a better fit with respect to multiple objectives. An example is used to illustrate the expanded decision support system, its outputs and their properties.     

From forecasting to ‘la prospective’ a new way of looking at futures

Although‘La Prospective’is not well known in the Anglo-Saxon forecasting literature, it has been for many years widely used in France and other Latin countries with considerable success. Lately, because of the inaccuracy of forecasting and the large forecasting errors that have been experienced, it is suggested that the Prospective approach can be used as a way of dealing with these problems. The main characteristics of‘La Prospective’are that it does not look at the future as a continuation of the past but rather as the outcome of the wishes of various actors and the constraints imposed on them by the environment. Its purpose is to assist in creating alternative futures and then select some alternative that allows for maximum freedom of action.     

Allocating the weights in the linear opinion pool

A standard approach to the combination of probabilistic opinions involves taking a weighted linear average of the individuals distributions. This paper reviews some of the possible interpretations that have been proposed for these weights in the literature on expert use. Several paradigms for selecting weights are also considered. Special attention is devoted to the Bayesian mechanism used for updating expert weights in the face of new information. An asymptotic result is proved which highlights the importance of choosing the initial weights carefully.