日径流预报贝叶斯回声状态网络方法

回声状态网络（ESN）相比传统递归神经网络,具有模型简单、参数训练速度快的特点．针对标准ESN因常采用线性回归率定模型参数容易出现过拟合问题,提出了基于贝叶斯回声状态网络（BESN）的日径流预报模型．该模型将贝叶斯理论与ESN模型相结合,通过权重后验概率密度最大化而获得最优输出权重,提高了模型的泛化能力．通过安砂和新丰江两座水库日径流预测实例表明,BESN模型是一种有效、可行的预测方法,与传统BP神经网络和ESN模型对比,进一步表明BESN模型具有更好的预测精度．     

基于相关向量机与随机有限元的筑坝材料参数不确定性反分析

由于面板堆石坝在设计、施工、建设中存在诸多不确定性因素,造成坝体沉降变形控制难的问题,对此建立了相关向量机(RVM)与随机有限元相结合的综合分析方法,对面板堆石坝材料参数的变异系数进行不确定性分析.首先,采用基于Cholesky离散方法的蒙特卡罗随机有限元方法,实现考虑堆石料力学参数变异性的随机有限元计算,从而构造出RVM的学习样本;随后,通过先进的机器学习算法——RVM建立反分析模型中输入-输出间的不确定性关系,有效地模拟材料参数与坝体沉降间的复杂非线性关系,使其能够代替随机有限元进行计算;最后,结合大坝实测沉降数据,利用遗传算法搜索确定筑坝材料的变异系数.通过分析公伯峡面板堆石坝应用实例表明:所建立的不确定性反分析模型综合考虑了数值计算以及输入-输出间的不确定性,可快速、精确地确定筑坝材料参数的变异系数,具有良好的工程应用前景和推广价值.     

集群企业群际协同创新的实现机制研究

机会主义行为是影响和制约集群企业群际协同创新的主要因素之一。构建两集群企业在合作创新过程中行为选择的博弈模型,分析进化均衡及其稳定性,探讨消除机会主义行为、促进合作创新的有效途径。研究表明:选择实力相当的合作伙伴,进行地位对等的合作对协同创新的成功最有利;企业在合作创新过程中应注重核心知识的保护,以降低合作伙伴进行机会主义行为的可能收益;完善惩罚机制可以有效促进实质协同。     

初始分布非均匀的疏散行人流实验研究和数学建模

针对初始分布非均匀的行人流疏散问题,通过定义行人在行走过程中的方向模糊可视域,充分考虑了行人在方向可视域选择上的差异性;通过引入出口处行人密度,有效实现了建筑物内初始分布非均匀的行人流动态疏散演化过程;通过定义和标准化处理影响转移概率的因素,改进了场域元胞自动机模型,并将仿真结果与相关已知模型做了对比分析,研究结果表明,改进模型避免了对影响转移概率相关因素的权重依赖;系统疏散时间与方向模糊可视域和出口处行人分布区域大小有关;如果行人在疏散过程中拥有一个较大的视野,系统能够在出口处适中区域内通过电子屏、语音提示等方式实时提供行人密度,就能够有效提高整个系统的疏散效率.     

基于ARIMA-RBF算法的月度降雨量预测研究

为了对月度降雨量进行科学预测,将ARIMA模型与RBF神经网络相结合,提出一种基于ARIMA-RBF耦合算法的月度降雨量预测模型。首先,利用ARIMA模型对月度降雨量线性部分进行拟合预测,计算ARIMA模型预测的残差;然后,利用RBF神经网络对ARIMA模型残差进行拟合预测;最后,利用RBF神经网络预测结果对ARIMA模型进行补偿修正,得到最终降雨量预测结果。将该方法用于重庆市沙坪坝月度降雨量实际预测中,预测结果精度高于单一ARIMA模型以及RBF神经网络,能够满足实际预测需求。结果表明:将线性拟合算法和非线性拟合算法结合起来用于月度降雨量预测是一种较为优越的算法。     

基于模型融合的复杂生产过程产品质量预测

复杂生产过程中产品质量与工艺参数间存在复杂的非线性关系,为提高产品质量预测准确性,本文提出了一种基于模型融合的复杂生产过程产品质量预测方法.首先,分别对复杂生产过程建立基于改进随机森林算法的整体预测模型与分段预测模型,其中,针对整体预测模型特征选择问题,提出了一种相关性分析与去冗余处理相结合的特征选择方法,针对分阶段预测模型存在误差累积问题,提出了一种误差修正机制.其次,利用Stacking集成学习算法实现整体预测模型与分阶段预测模型的融合,综合利用二者的预测优势得到对产品质量的最终预测结果.最后,以烟丝生产过程烘丝机入口烟丝含水率的预测为例,通过对比传统单模型的预测方法,验证了本文所提基于模型融合预测方法的准确性.     

基于云理论的径流不确定性推理模型研究

针对中长期径流预报中存在许多不确定性因素,本文引入云理论构建径流预报的不确定性推理模型（UR）.首先,该模型应用最大方差方法（MaxVar）对径流序列进行硬性分级,用级别概念表示径流分级区间,以期望（Ex）、熵（En）以及超熵（He）构成的云隶属函数描述径流级别概念的模糊性和随机性,实现分级区间软化,然后将径流量值进行属性转化,以此建立定性推理规则集,运用云算法进行径流不确定推理预报,成功实现径流序列不确定性传递;其次,对径流分级过程中超熵（He）参数确定进行了初探,对推理随机性输出结果进行统计分析,给出相应显著水平下的预报区间;最后,将该模型应用于南方某水库入库月径流预报中,并与广泛应用的最小二乘支持向量机（LSSVM）和ARMA模型进行比较分析,本文模型不仅具有较高的预报精度,而且能够进行区间预报,实例验证说明了模型的有效性和实用性.     

低成本辅助凸极式辐向永磁电机的鲁棒性优化设计

将铁氧体应用于具有聚磁能力的辐向永磁电机中可获得与稀土永磁电机相媲美的转矩密度,并兼具低成本的优势.为克服传统辐向永磁电机高转矩脉动的弊端,提出一种新型辅助凸极式转子,增强了转子的可塑性并增加优化参数的数目.采用基于响应面分析与多目标骨干粒子群优化算法相结合的多目标优化方法来提升电机的转矩性能.进一步地,考虑到在电机批量生产过程中不可避免地会受到误差、公差等不确定性因素的影响,将六西格玛设计方法和蒙特卡罗分析方法相结合,对电机进行鲁棒性优化.通过对比分析优化前后的电磁性能可知,所提出的新型电机可以在提高输出转矩的同时有效降低转矩脉动,且极大降低电机批量生产中的失效率.最后,实验结果验证了所提新型电机结构及其鲁棒性优化设计的准确性和可行性.     

基于PCA和BP神经网络的采空区稳定性评价模型研究

针对采空区稳定性评价因素的复杂性和相关性特点,提出主成分分析(PCA)与BP神经网络相结合的采空区稳定性综合评价方法。经综合分析确定以工程地质因素、采空区赋存结构参数、采动因素3个一级影响因素为基础的评价指标体系,以此为基础构建了采空区稳定性评价的BP神经网络评价模型。以某大型铅锌矿山地下采空区为例,应用CMS探测系统获取采空区相关数据生成采空区3D实体模型,并根据BP神经网络训练出的计算模型对采空区稳定性的等级进行评价。研究结果表明:PCA和BP神经网络相结合的方法使输入变量由13个减少为5个,避免了由于变量相关性带来的影响,简化了评价过程,结果更加合理。现场探测结果与BP神经网络计算结果相互支持。     

跨组织项目的合作伙伴关系风险仿真研究——以民机供应链为例

作为典型的跨组织项目,大飞机项目涉及多个供应商,并面临诸多风险。为了准确描绘复杂系统的风险管理过程,与目前普遍采用的单纯数学、解析风险模型的技术视角不同,本文利用智能体仿真建模来研究民机供应链风险沟通过程及风险沟通影响因素对风险管理绩效的影响,仿真结果表明:1)民机制造商在力量格局中处于劣势状态的情况下,在供应链合作伙伴间增加沟通次数能够降低整个项目周期的风险影响;2)在民机制造商的风险控制能力一定的条件下,适当规模的供应链合作伙伴关系能够降低项目风险。最后,文章提出了管理建议。     

Genome-wide association study to identify SNPs conferring risk of myocardial infarction and their functional analyses

Myocardial infarction might result from the interactions of multiple genetic and environmental factors, none of which can cause disease solely by each of themselves. Although molecular biological studies revealed that a number of proteins are possibly involved in its pathogenesis, little, if any genetic findings have been reported so far. To reveal genetic backgrounds of myocardial infarction, we performed a large-scale, case-control association study using 92,788 gene-based single-nucleotide polymorphism (SNP) markers. We have identified functional SNPs within the lymphotoxin-α gene (LTA) located on chromosome 6p21 that conferred susceptibility to myocardial infarction. Furthermore, we could identify galectin-2 protein as a binding partner of LTA protein. The association study further revealed that a functional SNP in LGALS2 encoding galectin-2, which led to altered secretion of LTA, also indicated a risk of myocardial infarction. A combined strategy of genetic and molecularcellular biological approaches may be useful in clarifying pathogenesis of common diseases.Received 7 March 2005; received after revision 22 April 2005; accepted 25 April 2005     

Neural networks and the multinomial logit for brand choice modelling: a hybrid approach

The study of brand choice decisions with multiple alternatives has been successfully modelled for more than a decade using the Multinomial Logit model. Recently, neural network modelling has received increasing attention and has been applied to an array of marketing problems such as market response or segmentation. We show that a Feedforward Neural Network with Softmax output units and shared weights can be viewed as a generalization of the Multinomial Logit model. The main difference between the two approaches lies in the ability of neural networks to model non‐linear preferences with few (if any) a priori assumptions about the nature of the underlying utility function, while the Multinomial Logit can suffer from a specification bias. Being complementary, these approaches are combined into a single framework. The neural network is used as a diagnostic and specification tool for the Logit model, which will provide interpretable coefficients and significance statistics. The method is illustrated on an artificial dataset where the market is heterogeneous. We then apply the approach to panel scanner data of purchase records, using the Logit to analyse the non‐linearities detected by the neural network. Copyright © 2000 John Wiley & Sons, Ltd.     

Vector smooth transition regression models for US GDP and the composite index of leading indicators

In this paper, I extend to a multiple‐equation context the linearity, model selection and model adequacy tests recently proposed for univariate smooth transition regression models. Using this result, I examine the nonlinear forecasting power of the Conference Board composite index of leading indicators to predict both output growth and the business‐cycle phases of the US economy in real time. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting output growth rates and median output growth rates: a hierarchical Bayesian approach

This paper describes procedures for forecasting countries' output growth rates and medians of a set of output growth rates using Hierarchical Bayesian (HB) models. The purpose of this paper is to show how the γ‐shrinkage forecast of Zellner and Hong ( 1989 ) emerges from a hierarchical Bayesian model and to describe how the Gibbs sampler can be used to fit this model to yield possibly improved output growth rate and median output growth rate forecasts. The procedures described in this paper offer two primary methodological contributions to previous work on this topic: (1) the weights associated with widely‐used shrinkage forecasts are determined endogenously, and (2) the posterior predictive density of the future median output growth rate is obtained numerically from which optimal point and interval forecasts are calculated. Using IMF data, we find that the HB median output growth rate forecasts outperform forecasts obtained from variety of benchmark models. Copyright © 2001 John Wiley & Sons, Ltd.     

Causality and forecasting in incomplete systems

In this paper we examine how causality inference and forecasting within a bivariate VAR, consisting of y(t) and x(t), are affected by the omission of a third variable, w(t), which causes (a) none, (b) one, and (c) both variables in the bivariate system. We also derive conditions under which causality inference and forecasting are invariant to the selection of a bivariate or a trivariate model. The most general condition for the invariance of both causality and forecasting to model selection is shown to require the omitted variable not to cause any of the variables in the bivariate system, although it allows the omitted variable to be caused by the other two. We also show that the conditions for one-way causality inference to be invariant to model selection are not sufficient to ensure that forecasting will also be invariant to the model selected. Finally, we present a numerical illustration of the potential losses, in terms of the variance of the forecast, as a function of the forecast horizon and for alternative parameter values—they can be rather large, as the omission of a variable can make the incomplete model unstable. © 1997 John Wiley & Sons, Ltd.     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model

This paper compares various ways of extracting macroeconomic information from a data‐rich environment for forecasting the yield curve using the Nelson–Siegel model. Five issues in extracting factors from a large panel of macro variables are addressed; namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data‐driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. Factor‐augmented methods perform well in relatively volatile periods, including the crisis period in 2008–9, when simpler models do not suffice. The macroeconomic information is exploited best by partial least squares methods, with principal component methods ranking second best. Reductions of mean squared prediction errors of 20–30% are attained, compared to the Nelson–Siegel model without macro factors. Copyright © 2011 John Wiley & Sons, Ltd.     

基于层次分析法与模糊综合评判法的考核评价方法研究

干部考核评价工作是选准、用好干部的关键环节。针对军队干部考核评价中存在的问题,通过采用层次分析法科学确定考评因素权重,结合民主评议和量化考核两项指标,引入模糊综合评判方法建立了德、能、勤、绩、考、管等全方面的考核评估模型对干部进行评价,使定性描述定量化,真实、客观、全面的对干部素质作出综合评价,不仅保证考评工作的公平、公正与合理,也为选拔任用优秀干部提供了有效的方法。