一类非线性回归模型的曲率降低问题

对于相当一般的一类非线性回归模型,从理论上证明了存在一种通过增加观测点降低非线性模型曲率的方法,使非线性模型有可能渐近于线性模型,这一结论对于实际应用具有重要的指导意义,并已在航天测量数据处理中取得了满意的效果。     

道路照明可见度计算模型研究

在城市道路照明中,可见度是对驾驶员的视觉可靠性起综合影响作用的评价指标,它对道路照明安全和节能起着非常重要的作用.本文介绍了道路照明可见度的概念及影响因素,详细研究了可运用于道路照明可见度设计中的小目标可见度的计算模型;从驾驶员的视功能和视觉舒适性出发,阐述了制定城市道路照明可见度标准的重要意义.     

港口非线性波浪三维耦合计算模型研究

建立了外域用差分求解高阶Boussinesq方程、内域用有限元求解Laplace方程的三维非线性波浪对船作用的时域计算耦合模型.研究了该类三维耦合模型的匹配条件,耦合求解过程和内域、外域公共区域长度的确定,探讨了内域有限元网格的剖分方法.把该耦合模型的计算结果与实验结果、内域用Euler方程的耦合模型计算结果进行了对比,结果表明该耦合模型具有满意的精度,适用于模拟较大区域内波浪对三维船等固定物体的作用,为今后近海岸大区域非线性波浪对三维非规则物体作用的时域计算和三维分区计算提供了参考.     

基于数字流域模型的多沙粗沙区产沙计算

黄河难以治理的症结是水少沙多，水沙不平衡。根据多年治黄实践，黄河中游多沙粗沙区的治理是黄土高原水土流失治理的重点，模拟计算多沙粗沙区的侵蚀产沙量意义重大。本文利用数字流域模型框架建立了黄土高原多沙粗沙区产流产沙数学模型，模型在岔巴沟小流域进行参数率定。应用率定后的模型计算了黄土高原多沙粗沙区1967、1978、1983、1994、1997五个典型年份汛期的产流产沙量，得到了多沙粗沙区的汛期径流深分布图和产沙模数分布图。计算结果能够较好地解释黄河流域的“少水多沙”现象，为多沙粗沙区的水土保持工作提供了科学依据。     

应变下管内电缆导体交流损耗计算模型

国际热核试验反应堆ITER（International Thermal-nuclear Experimental Reactor）上的CICC（Cable-in-Conduit Conductor）导体,将运行在大电流、快速励磁和瞬变磁场等复杂恶劣环境,为应对10T以上磁场的冲击,已部分采用铌三锡（Nb3Sn）导体.但缺乏应变下Nb3Sn导体交流（Alternating Current,AC）损耗机理的探索,为此,开展瞬变电磁场和宽应变区间的AC损耗计算方法研究,把复杂变化场中的耦合电流及穿透场简化为线圈励磁电流信号频谱效应,构造满足离散傅里叶转换（Discrete Fourier Transform,DFT）且包含频率、磁场及线圈特征等多参数的导体损耗计算技术.由模拟对比分析发现频谱算法的AC损耗更接近工程实际值,对快速励磁、特别是等离子体放电和破灭等情况,频谱算法和经典算法是相吻合的;对磁滞损耗和耦合损耗的相对误差计算,发现耦合损耗是线性累加的,磁滞损耗却并非完全如此,作为振幅、频率比以及相位角函数的相对误差的变化小于40%.结果显示傅里叶重组能取得满意效果.     

基于数字流域模型的多沙粗沙区侵蚀产沙计算

黄河难以治理的症结是水少沙多，水沙不平衡．根据多年治黄实践，黄河中游多沙粗沙区的治理是黄土高原水土流失治理的重点，模拟计算多沙粗沙区的侵蚀产沙量意义重大．利用数字流域模型框架建立了黄土高原多沙粗沙区产流产沙数学模型，模型在岔巴沟小流域进行参数率定．应用率定后的模型计算了黄土高原多沙粗沙区1967，1978，1983，1994和1997年这5个典型年份汛期的产流产沙量，得到了多沙粗沙区的汛期径流深分布图和产沙模数分布图．计算结果能够较好地解释黄河流域的“少水多沙”现象，为多沙粗沙区的水土保持工作提供了科学依据．     

网络在线交易动态信任计算模型与信誉管理机制

当前,在C2C网上拍卖交易领域,各种各样的恶意行为层出不穷,信任缺失信誉欺诈现象非常突出,因此,如何建立有效的信任机制已成为亟待解决的问题.在分析现有网络信任交易机制局限性的基础上,针对网络交易信任问题的动态性、匿名性、虚拟性等特点,提出一种基于多Agent系统的网络交易动态信任计算模型与信誉系统.模型包括3部分:第一部分为用户时域的信任度,旨在着重近期历史信誉状况对现期信任度的影响,激励用户采取一致的合作策略;第二部分是对信誉反馈评分的加权平均计算,权重部分主要引入了信誉反馈评分人的信任度(反馈评分的可信性)、交易价值(防止"信誉榨取")、时间折现("防范信誉波动")等因素;第三部分为社区贡献加权,旨在根据某一时域内,一用户对社区其他成员所采取的行为情况,增加或减少此时该用户的信任值,达到孤立信誉反馈提交、惩治欺诈行为的目的.建立了事前开展防范、事中进行协调、事后给予惩罚三位一体的欺诈约束机制,保障网络在线交易的安全性.理论分析和实验验证表明该机制能有效解决三方面问题:解决难以防范投机用户小额积誉、大宗榨取的问题;有效防止会员通过虚假交易或伪造身份进行欺诈;降低网络交易平台仲裁的工作量.     

氢燃烧单步反应模型和输运系数模型的建立

提出了更为合理的氢气-空气混合物的单步反应模型和输运系数模型建立方法.针对氢-空气单步反应方程式H2＋0.5O2→H2O,其反应速率模型为=1.13×1015[H2][O2]exp（46.37T0/T）mol/（cm3s）,输运系数模型为=K/CP=D=7.0×105T0.7g/（cms）.采用该模型和文献模型对ZND爆轰和自由传播层流火焰进行模拟,二者结果具有很好的一致性.另外还采有本文模型模拟了障碍通道内燃烧转爆轰过程,模拟结果与实验结果基本一致.验证了本方法和模型的正确性.     

用κ-ε紊流模型计算跌坎水流数值方法探讨

采用一种欧拉-拉格朗日（Eulerian-Lagrangian）相结合的剖开算子方法,用三角形网络离散流场,由κ-ε紊流模型数值求解了二维过跌坎水流.用特征线法解对流算子,用有限元法解扩散算子和圧力波松方程.算例表明,主要计算成果和试验结果能较好吻合,该法能很好适应解强非线性对流算子的复杂紊流流场.文中还对模型的边界条件处理进行了探讨.     

一种基于同步动力学模型的层次聚类方法

本文基于建模同步动力学行为的Kuramoto模型提出了一种新的有效层次聚类方法.本文提出的方法基于局部邻域的概念,能够实现稳定的局部同步聚类.通过不断扩大对象同步的邻域半径,所提出的方法能够实现层次化的同步聚类.此外,提出对象邻域闭包的概念,在对象间到达完全同步之前就能预测出聚类的形成,从而减少对象动态交互的时间.本文的方法不依赖于任何数据分布假设,无需任何手工参数设置,可以检测出任意数量、形状和大小的聚类.由于同步过程能够有效地规避离群点,该方法有较强的噪声数据抑制能力.在大量真实数据集和人工合成数据集上的实验结果表明本文的方法聚类准确率高,且运行时间较同类基准算法显著缩短.     

A hybrid approach with step-size aggregation to forecasting hierarchical time series

Hierarchical time series arise in various fields such as manufacturing and services when the products or services can be hierarchically structured. “Top-down” and “bottom-up” forecasting approaches are often used for forecasting such hierarchical time series. In this paper, we develop a new hybrid approach (HA) with step-size aggregation for hierarchical time series forecasting. The new approach is a weighted average of the two classical approaches with the weights being optimally chosen for all the series at each level of the hierarchy to minimize the variance of the forecast errors. The independent selection of weights for all the series at each level of the hierarchy makes the HA inconsistent while aggregating suitably across the hierarchy. To address this issue, we introduce a step-size aggregate factor that represents the relationship between forecasts of the two consecutive levels of the hierarchy. The key advantage of the proposed HA is that it captures the structure of the hierarchy inherently due to the combination of the hierarchical approaches instead of independent forecasts of all the series at each level of the hierarchy. We demonstrate the performance of the new approach by applying it to the monthly data of ‘Industrial’ category of M3-Competition as well as on Pakistan energy consumption data.     

Using a heterogeneous multinomial probit model with a neural net extension to model brand choice

The multinomial probit model introduced here combines heterogeneity across households with flexibility of the (deterministic) utility function. To achieve flexibility deterministic utility is approximated by a neural net of the multilayer perceptron type. A Markov Chain Monte Carlo method serves to estimate heterogeneous multinomial probit models which fulfill economic restrictions on signs of (marginal) effects of predictors (e.g., negative for price). For empirical choice data the heterogeneous multinomial probit model extended by a multilayer perceptron clearly outperforms all the other models studied. Moreover, replacing homogeneous by heterogeneous reference price mechanisms and thus allowing price expectations to be formed differently across households also leads to better model performance. Mean utility differences and mean elasticities w.r.t. price and price deviation from reference price demonstrate that models with linear utility and nonlinear utility approximated by a multilayer perceptron lead to very different implications for managerial decision making. Copyright © 2007 John Wiley & Sons, Ltd.     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.     

A BVAR model for the connecticut economy

A Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out-of-sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short- and long-term out-of-sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.     

A ground‐level ozone forecasting model for Santiago,Chile

A physically based model for ground‐level ozone forecasting is evaluated for Santiago, Chile. The model predicts the daily peak ozone concentration, with the daily rise of air temperature as input variable; weekends and rainy days appear as interventions. This model was used to analyse historical data, using the Linear Transfer Function/Finite Impulse Response (LTF/FIR) formalism; the Simultaneous Transfer Function (STF) method was used to analyse several monitoring stations together. Model evaluation showed a good forecasting performance across stations—for low and high ozone impacts—with power of detection (POD) values between 70 and 100%, Heidke's Skill Scores between 40% and 70% and low false alarm rates (FAR). The model consistently outperforms a pure persistence forecast. Model performance was not sensitive to different implementation options. The model performance degrades for two‐ and three‐days ahead forecast, but is still acceptable for the purpose of developing an environmental warning system at Santiago. Copyright © 2002 John Wiley & Sons, Ltd.     

混凝土材料三维弹塑性本构模型

非线性统一强度准则将材料的强度特性分解为4个相互独立的因素,由4个材料参数分别描述,在主应力空间内的强度面连续光滑,存在连续的偏导数.本文将非线性统一强度准则作为屈服函数,以塑性剪应变的函数作为硬化/软化参数,硬化/软化函数参考单轴压缩条件下的应力应变关系给出,建立了混凝土材料的非线性统一弹塑性本构模型.通过混凝土材料单轴、双轴和三轴试验结果对本构模型的验证,以及偏心受压构件试验结果对数值模拟结果的验证表明,所建立的非线性统一弹塑性本构模型可较好地描述混凝土材料的三维变形与强度特性,并可反映应变软化特性,将模型用于数值计算时易于获得收敛解,且具有较高的精确度和计算效率.     

A bayesian forecasting model for sequential bidding

In this paper we consider the problem facing a company in selecting the values of bids to submit on a sequence of contracts put out to tender. A simple-to-implement Bayesian forecasting model is presented, based on a steady Dirichlet process whose states are indexed by the possible bid decisions open to the company. The model gives an explicit algorithm for calculating the state probabilities, needing only data on the lowest bid made by the company's competitors. The flexibility of the basic model makes it a potentially powerful forecasting system for use by companies bidding for contracts.     

An outlier robust hierarchical Bayes model for forecasting: the case of Hong Kong

This paper introduces a Bayesian forecasting model that accommodates innovative outliers. The hierarchical specification of prior distributions allows an identification of observations contaminated by these outliers and endogenously determines the hyperparameters of the Minnesota prior. Estimation and prediction are performed using Markov chain Monte Carlo (MCMC) methods. The model forecasts the Hong Kong economy more accurately than the standard V AR and performs in line with other complicated BV AR models. It is also shown that the model is capable of finding most of the outliers in various simulation experiments. Copyright © 2004 John Wiley & Sons, Ltd.     

A threshold stochastic volatility model

This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.