方差分析法在制导仿真系统VV＆A中的应用

针对制导仿真系统研究,通过分析和研究方差方法的原理,提出了方差分析法在制导仿真系统VV&A中仿真结果验证和设备误差分析两个重要的应用途径.为了在仿真结果验证时充分利用不同试验条件下的仿真数据和靶试数据,基于方差分析法,对试验条件的不同取值影响仿真结果,并通过对具体试验数据的计算和分析,验证了可行性;对制导仿真系统的误差分析时,提出了设备误差对静态仿真结果影响,丰富了仿真设备误差分析的手段.实践证明,方差分析法是制导仿真系统VV&A中的一种非常有效的辅助手段.     

煤矿井下微机电系统陀螺随机误差辨识

针对常用随机误差辨识方法不能揭示潜在的误差源、很难分离出具体随机误差、数据采集时间过长等问题,利用Allan方差分析法对煤矿井下微机电系统(MEMS)陀螺随机误差进行辨识。介绍了Allan方差分析法原理,利用Allan方差分析法对MEMS陀螺实测数据进行处理,给出了Allan标准差曲线,通过最小二乘拟合得到MEMS陀螺的主要随机误差系数。实验结果验证了Allan方差分析法用于MEMS陀螺随机误差辨识的有效性。     

计算机静态仿真结果统计检验方法

根据系统真实输出与仿真输出结果的一致性与否,研究了仿真模型的验证方法,主要论述系统试验结果为静态数据情况下的检验方法.当仿真试验结果为静态数据,它与真实系统的试验结果是否相匹配的验证,从数理统计的角度来讲,其实质是检验这两种来源的试验数据是否属于同一分布母体.针对正态分布计算机仿真结果分别研究了仿真试验结果为大样本与小样本情况下的分布参数与母体特征值的统计检验方法,并以此来判断仿真试验结果正确与否.     

战略导弹精度仿真中仿真模型的校验研究

仿真的一个核心问题是如何进行仿真模型的检验(或称验证)和校正。本文以战略导弹精度仿真为例,对仿真模型进行检验,利用BET方法重构弹道,得到校正仿真模型所需的数据,据此,提出一种基于滤波算法的模型校正方法。     

步降应力加速寿命试验的可靠性仿真

研究武器装备寿命实验准确评估可靠性问题,为进一步完善步降试验的统计分析方法,建立依赖于加速模型的数据折算公式,再对步降试验下的三步分析法做出改进,改进后的方法在处理数据时更具灵活性,且计算复杂度有所降低.进而,为比较步进试验与步降试验下三步分析法的精确性与稳定性,首次利用Monte - Carlo仿真法对两试验下的三步分析法进行仿真.仿真结果显示,相比步进试验,步降试验能显著提高三步分析法的精度和稳定性,说明了步降应力试验具有优越性为可靠性评估提供了依据.     

一种X波段雷达反演算法的研究

研究了X波段雷达数据反演算法,为了验证反演算法的准确性,利用Longuet-Higgins海浪模型,通过选用适当的海浪谱和方向函数,仿真了三维随机海浪数据,利用反演模型得到的数据和仿真的海浪数据进行拟合,结果表明反演算法能够有效地实现海浪相关信息的提取;为了进一步检验算法的实用性,将反演算法得到的参数和实际的波浪浮标数据进行了对比,结果表明反演算法有一定的可行性。     

基于飞参数据的飞行仿真模型验证方法研究

仿真模型验证已成为仿真研究的有机组成部分。为了更好地对飞行训练模拟器飞行仿真模型进行可信性评估,提高飞行训练模拟器飞行仿真模型的逼真性,将飞参数据应用于验证飞行训练模拟器飞行仿真模型的可信性。提出飞行仿真模型验证的验证方案,并对方案中的飞参解译、飞参数字滤波、飞行阶段识别及飞参数据插值处理等技术进行研究。介绍仿真模型验证量化评估的方法,包括时域分析法和频域分析法,并将这几种方法应用于某型飞行训练模拟器飞行仿真模型的验证,验证结果表明,利用飞参数据验证飞行仿真模型的方法完全可行。     

基于扩展贝叶斯理论的飞机液压系统效能评估北大核心

飞机液压系统准确效能评估,是分析实际作业中飞机液压系统运行情况及其结构功能优化设计的基础。针对传统效能评估方法普遍依赖专家决策的问题,以贝叶斯理论为基础,提出了一种考虑评估指标数据可信度的扩展贝叶斯效能评估模型。模型利用提出的改进灰色关联分析法获得评估指标数据的可信度量化值,将其与指标数据作为评估指标的二维数组表达,并通过扩展贝叶斯理论进行专家数据和仿真数据等异构数据的分析融合。以某型飞机液压系统为例,验证了上述方法在飞机液压系统效能评估中的有效性,并可用于多源数据的系统效能评估中。     

基于虚拟现实的装甲装备维修性定量指标验证方法研究

对维修性定量指标验证的研究现状进行分析,提出构建基于虚拟现实的产品维修性定量指标验证系统;首先,根据系统需求构建出系统的总体框架;其次,对系统搭建过程和仿真过程中的关键技术进行阐述;再者,对国军标中维修性定量参数估计模型进行改进,构建适用于复杂装备的虚拟仿真MTTR估计模型;最后,在验证系统中对某型车辆传动装置进行MTTR虚拟仿真试验,并运用MTTR估计模型进行MTTR验证;对比得到,通过虚拟仿真试验得到的验证结果与实车试验数据得到的结果基本一致,具有较高的可信性;该方法为在产品研制设计阶段并行维修性定量指标的验证工作提供了非常重要的参考价值。     

极点与状态方差约束下的动态输出反馈可靠控制

研究了线性不确定系统区域极点与状态方差约束下的动态输出反馈可靠控制器的设计问题．首先在连续型执行器故障模型下，利用线性矩阵不等式方法，建立了容错控制中区域极点指标与方差上界指标间的相容性理论；然后，应用极值原理分析了与区域极点指标相容的方差上界指标的取值范围；最后，给出了同时满足区域极点与状态方差约束下的动态输出反馈可靠控制器的设计步骤．仿真实例验证了结果的有效性．     

Knowledge-based method for the validation of complex simulation models

In traditional research, the validation of simulation models is mainly based on statistical analysis and simulation error evaluating. As simulation models become more and more complex, simulation behavior is more complicated and heavily dependent on simulation conditions, traditional method cannot be applied directly. On the other hand, measured data needed for traditional methods cannot always be available. What’s more, the validation of complex models is usually costly and time-consuming.This research makes effort to settle the problems above. It provides three aspects of advantage for model validation. First, complicated simulation behavior is abstracted and classified as five categories, among which behavior relationship and aggregative behavior is unique for complex simulation models. And the analysis and validation method of each kind of behavior is proposed. Second, besides measured data of real system, it proposed to utilize experience of expert and other kind of domain knowledge for validation task. And third, the simulation analysis and validation method proposed can be implemented in knowledge system and accomplish validation task automatically.Simulation output analysis is the most important step in model validation. Besides classical continuous dynamic fitting and statistical consistency analysis methods, we propose a domain knowledge-based method for the validation of relationship among behavior segments and aggregative behavior which is unique for complex simulation models. Domain knowledge takes the role of reference in model validation, and it varies from measured data of real system to qualitative experience of experts. Knowledge-based system is implemented based on domain knowledge and validation techniques proposed.Validation of electromagnetic rail gun simulation models are introduced as an example. It has been found that this method can provides an automatic validation way for complex simulation models, and validation tasks can be accomplished efficiently.     

某型导弹系统仿真模型验证

仿真模型验证是仿真可信性研究的重要组成部分，有着极其重要的意义。模型验证就是比较仿真系统输出和实际系统输出的一致性，分为静态性能验证和动态性能验证。该文在分析研究各种仿真模型验证方法的基础上，主要采用工程上常用的bayes方法和窗谱分析法对某型导弹系统仿真模型进行了定量验证，最后给出结论并讨论了这两种方法的局限性。     

基于谱估计的雷达信号模型验证方法

雷达信号模型是雷达系统仿真的重要组成部分,因此雷达信号模型验证在系统可信性分析中具有重要意义。采用经典谱估计和最大熵谱估计两种方法对仿真产生的两组雷达信号进行谱估计,提出了利用相关系数方法在频域对模型进行验证,对比分析了两种方法的优缺点,并对某雷达仿真系统与某次试验采集得到的数据进行了验证,表明此雷达仿真系统具有较高的可信度。     

Computer Modeling and Simulation: Increasing Reliability by Disentangling Verification and Validation

Verification and validation (V&V) of computer codes and models used in simulations are two aspects of the scientific practice of high importance that recently have been discussed widely by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to the model’s relation to the real world and its intended use. Because complex simulations are generally opaque to a practitioner, the Duhem problem can arise with verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or the model’s general inadequacy to its target should be blamed in the case of a failure. I argue that a clear distinction between computer modeling and simulation has to be made to disentangle verification and validation. Drawing on that distinction, I suggest to associate modeling with verification and simulation, which shares common epistemic strategies with experimentation, with validation. To explain the reasons for their entanglement in practice, I propose a Weberian ideal–typical model of modeling and simulation as roles in practice. I examine an approach to mitigate the Duhem problem for verification and validation that is generally applicable in practice and is based on differences in epistemic strategies and scopes. Based on this analysis, I suggest two strategies to increase the reliability of simulation results, namely, avoiding alterations of verified models at the validation stage as well as performing simulations of the same target system using two or more different models. In response to Winsberg’s claim that verification and validation are entangled I argue that deploying the methodology proposed in this work it is possible to mitigate inseparability of V&V in many if not all domains where modeling and simulation are used.

     

Wavelet ANOVA approach to model validation

Model validation is that critical component in the simulation development process that ensures a model is truly representative of the system that it is meant to model. Although there are numerous validation techniques described in the literature, many of these techniques still require some amount of subjective analysis in order to assess validity. This is particularly true with dynamic simulation output. To reduce or eliminate this subjectivity, this paper proposes a validation process that uses wavelet analysis of variance (WANOVA) as an effective method to statistically accept or reject a model as valid. This WANOVA validation approach performs statistical inference in the time-frequency domain to take advantage of wavelet sparsity and decorrelation. This process uses a test statistic based on thresholded wavelet coefficients to test the null hypothesis that the set of system data and model data are statistically equivalent. The validation technique is illustrated using a simulation study and empirical data from an automobile crash study.     

考虑相关性的多元输出仿真模型验证方法

复杂仿真模型一般具有多个不同类型且带有相关性的输出,现有验证方法存在变量信息缺失、变量相关性度量不准确等问题.为此,提出基于变量选择和概率分布差异相结合的多变量仿真结果验证方法,考虑不确定性的影响,对选取到具有相关性的多个变量进行联合验证.首先,引入分形维数和互信息方法对多元异类输出进行相关性分析,提取相关变量子集.而后对相关变量子集中的各变量提取数据特征,进而计算各相关变量子集关于数据特征的联合概率分布,采用概率分布差异法度量仿真输出和参考输出联合概率分布的差异,并将其转化为一致性程度;在此基础上综合多个验证结果得到模型可信度.通过实例应用及对比实验,验证了方法的有效性.     

Approximate estimation in generalized linear mixed models with applications to the Rasch model

This article discusses two different approaches to estimate the difficulty parameters (fixed effects parameters) and the variance of latent traits (variance components) in the mixed Rasch model. The first one is the generalized estimating equations (GEE2) which uses an approximation of the marginal likelihood to derive the joint moments whilst the second approach uses the maximum of the approximate likelihood. We illustrate these methods with a simulation study and with an analysis of real data from a quality of life.     

Random generation of industrial pipelines’ data using Markov chain model

Random generation of data sets is a vital step in simulation modeling. It involves in generating the variation associated with the real system behavior. In the industrial fabrication of construction components, unique products such as pipelines are produced. The fabrication processes are dependent on pipelines features, and complexity; randomly generating pipelines structure is imperative in the simulation of such processes. This paper investigates the nature of industrial pipelines and proposes a Markov chain model to randomly generate pipelines data structure. The performance of Markov chain model was tested against real pipelines through a three-stage validation process. The validation process includes (1) a validation based on the number of components and the pipelines components correlation analysis, (2) clustering-based model validation, and (3) model validation using similarity distances between pipelines feature vectors. The Markov chain model was found to generate a reasonable pipelines data structure when compared with real pipelines. It was found that 89% of the generated pipelines share similar properties equivalent to 0.88 (a scale from 0 (not identical) to 1 (identical)) to 85.5% of the original pipelines.     

A methodological approach for model validation in faster than real-time simulation

When successfully dealing with time constraints, faster than real-time simulation (FRTS) improves process control capabilities through providing short-term predictions. Validation of a simulation model is accomplished through comparing system observations and model data corresponding to the same time points. A methodological approach is necessary for the realization of this comparison, considering the time-dynamic system behaviour and the potential deviations between the model and the system. A seven-step method is proposed, emphasizing the following issues: determining when predictions should be considered as valid, accomplishing validation on the basis of the available model and system data, considering that not all deviations between the model and the system are of equal significance, and indicating potential system changes, so that the model can be modified in real time. Computer networks are used as an example domain, due to their multi-entity structure and time-dynamic behaviour, offering excellent test cases for evaluating the proposed method. Experimental FRTS results from the application of the proposed method to the network domain are also presented.