蒙特卡罗数值模拟法在随机参数转子动力学响应中的应用

本文运用模拟量输出卡和功率放大器生成符合正态分布的随机电流,根据已有的支撑刚度和随机电流之间的线性关系,得出支撑刚度的均值和标准差。在已有的转子系统模型上,利用蒙特卡罗数值模拟方法对随机参数进行模拟计算,得出支撑刚度参数的改变对转子动力学响应位移的影响。     

多阶段多商品广告预算问题的随机优化模型

多阶段多商品广告预算问题的随机优化模型,可以看做是确定性优化模型的扩展。基于MAB问题提出了一种随机优化模型,解决实际问题中参数的不确定性,该模型是一个凸优化模型,通过与确定性模型的比较,可以看到随机优化模型更加有效。     

情景模拟技术在我国公务员培训中的有效应用

当前国际国内环境的变化对我国公务员的能力提出了更高的要求,情景模拟技术对于公务员能力的提升有其显著的适用性。本文在比较传统培训方式与情景模拟式培训的基础之上,分析了情景模拟式培训在公务员培训中的优势,构建了具体的应用流程,并指出提高其有效性的要点。     

二元光学器件表面表征方法研究

二元光学器件是一种表面微细结构组成的衍射光学元件。其制作工程中存在的加工误差主要有系统刻蚀误差、对准误差、随机台阶刻蚀深度误差、随机台阶刻蚀宽度误差等。其表面结构的形状及其偏差对其使用性能将产生严重的影响。探索出有效的二元光学器件表面表誊征方法是保证其制作工艺及系统有良好性能的前提和主要手段。本文介绍了一种幅度参数表征法，并给出了参数表征法中各参数的数学模型。     

三阶锁相环环路滤波器参数设计

锁相环在通信、遥测、导航等领域有着广泛的应用,三阶锁相环由于其频率斜率跟踪能力,越来越受到重视,特别是深空探测的极窄带应用。利用系统稳定性分析方法和高阶系统分析理论,分别对两种模型的二阶环路滤波器,即理想二阶滤波器和三参数滤波器模型,推导了参数设计公式,给出三阶锁相环设计参数的模拟及数字环路公式,并与JPL数字锁相环(DPLL)的设计参数经验公式进行比较。仿真结果表明,3种设计方法近似相同,而所推导的参数设计方法优点在于可以灵活配置系统的零、极点的位置以及阻尼系数等多种参数,为各种变带宽和自适应算法提供理论和应用基础。     

多媒体下大学生写作练习情景模拟设计

写作课的魅力在写,写作课的优势是让学生在学习写作的过程中充分感受写作带来的快乐,同时练习写成一篇篇能充分产生现实效用的应用文章,进而形成较强的写作能力。多媒体技术集声音、影像、文字图案等信息传播技术手段于一体,能充分真实地再现社会生活的现实性情形,实现场景模拟的"仿真"效果,是激发学生写作热情的上佳方法,多媒体环境下的写作练习的情景模拟设计可采用情景融入设计、情景参与设计、情景代入设计的方式实现。     

动态财务分析方法的比较优势分析

对于目前欧美流行的动态财务分析方法,本文通过比较它与传统静态评估方法、其他动态评估方法,并进一步分析动态财务分析中的情景分析和随机模拟方法,探讨动态财务分析方法的优势所在。     

信用风险模型比较研究

信用风险模型在现代金融界正发挥着越来越重要的作用,本文比较了现在主流的被实务界应用的信用风险评估模型,从而来探讨他们所适用的不同的情景,不同的优缺点.总的来说市场化模型和历史模型各有各自的优势,需要根据实际情况进行不同的选择.     

对俘虏产品定价策略在餐饮业应用的经济学思考

俘虏产品定价策略是组合定价中的一种基本定价策略,在餐饮业得到一定程度的应用。由于餐饮业自身的特殊性,应用俘虏定价策略有其特定的优势和存在的问题。运用营销学、经济学的有关理论与方法进行剖析,提出解决问题的思路,并建立简易的定价模型,以使俘虏定价策略在餐饮业得到更为广泛的应用。     

浅议直线预测模型参数的估计方法

直线趋势模型是预测模型中应用较广的一种。估计直线模型参数的方法,目前主要有直观法、平均法、最小平方法、三点法、指数平滑法和折扣最小平方法。估计直线预测模型参数的新方法──“侧近两点法”。该法具有侧近性特点并且使用方便,具有较大的推广价值。     

A Guadagni-Little Likelihood Can Have Multiple Maxima

Despite many advances in marketing models, the Guadagni-Little (1983) model is still in widespread use by both practitioners and academics. For many new marketing models, the Guadagni-Little model serves as a benchmark. The key variable that allows the Guadagni-Little model to accurately fit data is the loyalty variable, which is an exponential smoothing of past purchases. In this paper, I show that inclusion of this variable in the logit model results in a likelihood function that can have multiple maxima. I am able to demonstrate this using simulated data and actual household scanner panel data. In addition, I document a systematic relationship between the loyalty coefficient and the loyalty smoothing parameter. Insight for this systematic relationship and the multiple maxima is obtained by recognizing a trade-off between capturing household heterogeneity and state dependence. Finally, in the Guadagni-Little model extreme parameter values capture two different idealized forms of consumer behavior. However, reported studies rarely find these extreme parameter values. I show that procedures commonly used to initialize loyalty biases against these extreme parameter values. This bias offers some explanation for the observed empirical regularity in Guadagni-Little parameter estimates and suggests that researchers should be cautious concluding these parameters capture regularity in consumer behavior.     

A Guadagni–Little Likelihood Can Have Multiple Maxima

Despite many advances in marketing models, the Guadagni–Little (1983) model is still in widespread use by both practitioners and academics. For many new marketing models, the Guadagni–Little model serves as a benchmark. The key variable that allows the Guadagni–Little model to accurately fit data is the loyalty variable, which is an exponential smoothing of past purchases. In this paper, I show that inclusion of this variable in the logit model may result in a likelihood function that can have multiple maxima. I am able to demonstrate this using simulated data and actual household scanner panel data. In addition, I document a systematic relationship between the loyalty coefficient and the loyalty smoothing parameter. Insight for this systematic relationship and the multiple maxima is obtained by recognizing a trade-off between capturing household heterogeneity and state dependence. Finally, in the Guadagni–Little model extreme parameter values capture two different idealized forms of consumer behavior. However, reported studies rarely find these extreme parameter values. I show that procedures commonly used to initialize loyalty biases against these extreme parameter values. This bias offers some explanation for the observed empirical regularity in Guadagni–Little parameter estimates and suggests that researchers should be cautious concluding these parameters capture regularity in consumer behavior.     

信道特性对OFDM信道估计中导频模式选择的影响

分别介绍了基于梳状导频和块状导频的OFDM信道估计的原理,重点研究了上述两种导频在不同的信道环境中的信道估计性能.仿真分析展示了两种导频模式对传输信道的不同参数的敏感程度,由此给出了在不同的传输信道条件下导频模式的选择依据.     

Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity

We propose an efficient individually adapted sequential Bayesian approach for constructing conjoint-choice experiments, which uses Bayesian updating, a Bayesian analysis, and a Bayesian design criterion to generate a conjoint-choice design for each individual respondent based on the previous answers of that particular respondent. The proposed design approach is compared with three non-adaptive design approaches, two aggregate-customization approaches (based on the conditional logit model and on a mixed logit model), and the (nearly) orthogonal design approach, under various degrees of response accuracy and consumer heterogeneity. A simulation study shows that the individually adapted sequential Bayesian conjoint-choice designs perform better than the benchmark approaches in all scenarios we investigated in terms of the efficient estimation of individual-level part-worths and the prediction of individual choices. In the presence of high consumer heterogeneity, the improvements are impressive. The new method also performs well when the response accuracy is low, in contrast with the recently proposed adaptive polyhedral approach. Furthermore, the new methodology yields precise population-level parameter estimates, even though the design criterion focuses on the individual-level parameters.     

侦察参数精度对机载SAR假目标干扰的影响

针对实施机载合成孔径雷达(SAR)假目标欺骗干扰所需引导参数存在测量误差的问题,首先,分析了假目标欺骗干扰调制信号模型,并给出了干扰信号所需侦察参数;然后,分析了机载平台与信号参数误差对假目标的位置、幅度及分辨率的影响,指出了假目标在SAR图像中的“折叠”现象,并分析了产生原因;最后,对参数误差的影响进行了定量仿真分析。理论分析与仿真结果表明,SAR的定位误差与假目标的成像位置偏移量大致呈线性关系。当速度误差为2%、方位向定位误差为100 m时,假目标的方位向分辨率降低约1倍。     

采用图像处理的跳频信号参数盲估计

针对短波复杂信道环境下的跳频信号参数估计问题,提出了一种基于图像处理的跳频信号参数盲估计算法。该算法在时频分析的基础上采用灰度共生矩阵提取信号的纹理特征,通过对纹理特征量的分割实现信号与背景噪声的分割,并运用形态学滤波去除二值化后产生的椒盐噪声;然后根据连通区域标记得到的各个信号在时频图中的位置信息来聚类,从而去除定频、突发等干扰信号,分选出跳频信号;最后根据分选出的跳频信号提取其跳频频线并进行修正,估计出跳频信号的跳周期、跳变时刻和跳频频率。仿真实验表明,该算法切实有效,能够在较低的信噪比条件下精确地估计出跳频信号的参数。     

Estimating time-varying parameters in brand choice models: A semiparametric approach

Nowadays, brand choice models are standard tools in quantitative marketing. In most applications, parameters representing brand intercepts and covariate effects are assumed to be constant over time. However, marketing theories, as well as the experience of marketing practitioners, suggest the existence of trends or short-term variations in particular parameters. Hence, having constant parameters over time is a highly restrictive assumption, which is not necessarily justified in a marketing context and may lead to biased inferences and misleading managerial insights.In this paper, we develop flexible, heterogeneous multinomial logit models based on penalized splines to estimate time-varying parameters. The estimation procedure is fully data-driven, determining the flexible function estimates and the corresponding degree of smoothness in a unified approach. The model flexibly accounts for parameter dynamics without any prior knowledge needed by the analyst or decision maker. Thus, we position our approach as an exploratory tool that can uncover interesting and managerially relevant parameter paths from the data without imposing assumptions on their shape and smoothness.Our approach further allows for heterogeneity in all parameters by additively decomposing parameter variation into time variation (at the population level) and cross-sectional heterogeneity (at the individual household level). It comprises models without time-varying parameters or heterogeneity, as well as random walk parameter evolutions used in recent state space models, as special cases. The results of our extensive model comparison suggest that models considering parameter dynamics and household heterogeneity outperform less complex models regarding fit and predictive validity. Although models with random walk dynamics for brand intercepts and covariate effects perform well, the proposed semiparametric approach still provides a higher predictive validity for two of the three data sets analyzed.For joint estimation of all regression coefficients and hyperparameters, we employ the publicly available software BayesX, making the proposed approach directly applicable.     

基于Ozaktas算法的LFM信号参数估计误差分析

Ozaktas算法因其运算复杂度低、精度高、提出时间早而成为目前对LFM信号进行处理 时最为常用的离散分数阶Fourier变换算法,但其附加的量纲归一化对LFM信号参数估计存在 影响。为此,在对LFM信号参数估计建模基础上,分析了基于Ozaktas算法的参数估计二维离 散网格效应,并进一步得到了影响初始频率和调频率估计精度的因素。可以发现：在满足采 样定理条件下,基于Ozaktas算法的LFM信号参数估计能保持较好的估计精度,且在一定程度 上可以通过增大采样频率或减小采样时长来进一步提高估计精度。最后,通过仿真分析验 证了上述理论推导的正确性。     

组合幂调频信号的时频移估计

为了准确估计接收信号的时频移参数和降低估计复杂度,设计了一种组合幂调频（CPFM）信号,该信号由具有时间间隔的正负幂调频（PFM）信号构成,并提出了基于CPFM信号的时频移估计算法。在算法中首先将CPFM信号降阶,然后独立地估计时延和频移,即采用三次相位函数（CPF）估计时延,通过正负PFM相位相消后再估计频移。相比于分数阶傅里叶变换（FRFT）时频移估计算法,所提算法避免了时延估计对频移估计的影响,而且仅需两次一维搜索,降低了计算量。仿真结果表明,该算法能准确地估计CPFM信号的时延和频移,并且频移估计均方误差（MSE）接近克拉美劳下界（CRLB）。     

GPS辅助的被动成像导航系统的研究

本文介绍的是一种ＧＰＳ辅助的被动成像导航系统。该系统将ＧＰＳ接收机和被动成像传感器组合在一起，能提供完整的位置、速度、角速度和高度信息。在ＧＰＳ信息的辅助下，不仅解决了被动成像传感器所引起的高度／速度模糊问题，而且还大大简化了求解角速度的方程。本文给出了角速度估计和ＧＰＳ测量噪声对估计精度影响的理论分析和计算机仿真结果。仿真表明，即使在严重的ＧＰＳ测量噪声的影响下，运动参数仍可精确地计算出来。