A novel approximation of basic probability assignment based on rank-level fusion

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment (BPA) approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

一种有效的基于DSmT的合成公式

针对DSmT在解决强冲突证据融合问题时存在的不足,提出了一种有效的冲突证据合成方法,即在保留冲突焦元的基础上对冲突量进行重新分配。仿真结果表明,新的合成公式提高了目标识别的可靠性与合理性,即使对于高度冲突的证据,也能够取得理想的合成结果。     

自组织模糊CMAC神经网络及其非线性系统辨识

 针对CMAC的特点,提出了联想度的概念,并由此设计了一种自组织模糊 CMAC神经网络( SOFC-MAC)及其学习算法,证明了SOFCMAC能以任意精度对非线性特性一致逼近。该网络具有学习速度快,逼近精度高及局部泛化能力等特点。歼击机系统特征模型辨识仿真验证表明了该方法的有效性。     

Epistemic uncertainty quantification in flutter analysis using evidence theory

Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and method selection uncertainty, on the basis of information from limited experimental data of uncertain parameters. Two uncertain variables of the actuator coupling system with unknown probability distributions, that is bending and torsional stiffness, which are both described with multiple intervals and the basic belief assignment(BBA) extricated from the modal test of actuator coupling systems, are taken into account. Considering the difference in dealing with experimental data by different persons and the reliability of various information sources, a new combination rule of evidence––the generalized lower triangular matrices method is formed to acquire the combined BBA. Finally the parametric uncertainty and the epistemic uncertainty of flutter analysis method selection are considered in the same system to realize quantification. A typical rudder of missile is selected to examine the present method, and the dangerous range of velocity as well as relevant belief and plausibility functions is obtained. The results suggest that the present method is effective in obtaining the lower and upper bounds of flutter probability and assessing flutter risk of structures with limited experimental data of uncertain parameters and the belief of different methods.     

A new non-specificity measure in evidence theory based on belief intervals

In the theory of belief functions, the measure of uncertainty is an important concept, which is used for representing some types of uncertainty incorporated in bodies of evidence such as the discord and the non-specificity. For the non-specificity part, some traditional measures use for reference the Hartley measure in classical set theory;other traditional measures use the simple and heuristic function for joint use of mass assignments and the cardinality of focal elements. In this paper, a new non-specificity measure is proposed using lengths of belief intervals, which represent the degree of imprecision. Therefore, it has more intuitive physical meaning. It can be proved that our new measure can be rewritten in a general form for the non-specificity. Our new measure is also proved to be a strict non-specificity measure with some desired properties. Numerical examples, simulations, the related analyses and proofs are provided to show the characteristics and good properties of the new non-specificity definition. An example of an application of the new non-specificity measure is also presented.     

基于云模型和改进D-S证据理论的目标识别决策方法

在目标识别决策系统中,多探测器多源信息融合的模糊性和不确定性以及各探测周期所得信息的冲突互斥会造成目标识别决策不精准。为解决这一问题,提出基于云模型和改进D-S （Dempster-Shafer）证据理论的目标识别决策方法。首先,将目标识别准确性这一语言评价值划分为不同评价区间等级,以不同评价等级标准云为参照将各探测器各探测周期所得信息转化为云决策矩阵,得出各周期各等级隶属度,进而构建出基本概率分配函数（mass函数）;其次,基于证据理论引入冲突度、差异度、离散度3类衡量冲突大小的参数,定义了一种新的证据冲突参数,同时改进证据冲突融合算法,对各探测器各周期证据体进行修正并融合;再次,结合各探测器权重加权得出各目标综合识别决策的mass函数对目标进行决策;最后,结合算例,验证该方法的适用性,并与其他方法相对比验证了本文方法的优越性。     

基于近似技术的高亚声速运输机机翼气动/结构优化设计

探索基于近似技术的高亚声速运输机机翼气动/结构多学科设计优化方法,建立了基于近似技术的多学科设计优化框架。气动学科采用全速势方程加黏性修正进行翼身组合体跨声速流动的气动计算,结构学科采用有限元分析方法进行应力与变形计算。采用均匀设计法给出若干样本点,分别采用二次响应面、Kriging模型和径向基神经网络等多种近似技术,构造气动学科和结构学科的近似分析模型,并对几种近似模型精度进行了分析和比较。研究发现,Kriging模型和二次响应面具有几乎等同的较高的近似精度,神经网络的近似精度则较差,由于二次响应面计算量更小,故最终选定为机翼设计优化的近似方法。以升阻比和结构重量为目标,考虑升力、机翼面积以及应力和应变约束条件,对运输机机翼4个外形参数和4个结构参数进行多目标、多约束优化设计。优化后的机翼具有较好的气动/结构综合性能,表明本文方法是可行的。     

Approximating the SNR value in detection problems

The inverse problem of finding the required signal-to-noise ratio (SNR) given a set of probability parameters and number of samples is a nontrivial problem. Several past attempts have proposed simple approximations for the SNR, but the achieved accuracy varied across the parameter space and was at times poor. The approximation equations proposed here are considerably more accurate over a larger parameter space.     

基于PNN-DS信息融合模型的目标识别方法

针对目标识别中常用BP—DS信息融合方法识别率低,运行速度慢,抗噪性差等问题,提出一种基于PNN网络和DS证据的信息融合方法。该方法不仅综合了证据理论在处理不确定信息方面的优点和神经网络在数值逼近上的长处,利用神经网络和证据推理算法获取了基本概率赋值,同时突出了PNN网络在处理多传感器信息的准确性和运算速度上都要优越于BP网络的特点。     

民用飞机飞行模拟机数据包试飞任务优化结合方法研究

飞行模拟机数据包试飞用于获取飞行模拟机研制及鉴定所需的试飞数据,涉及多项试飞科目。为提高试飞效率、降低试飞成本,针对飞行模拟机数据包试飞与型号试飞的优化结合方法进行了研究。通过分析飞行模拟机研制进度要求、型号试飞规划、型号构型到位计划,以及飞行模拟机数据包试飞和型号试飞各试飞科目的试飞要求等要素,分别在试飞科目分析阶段和试验点分析阶段提出了不同的优化结合原则,包括试飞方法相似原则、科目类别相似原则、前置试验满足原则及构型要求兼顾原则。并给出了直接结合、试验点连续结合、形态变换结合及架次结合四种优化结合方法,以及优化结合方法所适用的飞行模拟机数据包试飞阶段。该优化结合方法已在实际型号飞行模拟机数据包试飞任务规划中得到应用,起到了减少试飞任务量的作用。     

一种基于实数粗集空闻的自组织映射方法及在模式识别上的应用

提出一种实数粗糙集，避免了经典粗糙集必须经过离散化处理的环节；并且用实数粗糙集的下、上近似集的精确概念划分自组织映射的输出结果，使得修改后的映射结果中各类样本点之间有明显的间隔，易于进行分类识别。最后通过对某型歼击机的舵面故障的模式识别仿真验证了其方法的正确性和有效性。     

一种基于实数粗集空间的自组织映射方法及在模式识别上的应用

提出一种实数粗糙集,避免了经典粗糙集必须经过离散化处理的环节;并且用实数粗糙集的下、上近似集的精确概念划分自组织映射的输出结果,使得修改后的映射结果中各类样本点之间有明显的间隔,易于进行分类识别.最后通过对某型歼击机的舵面故障的模式识别仿真验证了其方法的正确性和有效性.     

一种修正的DS证据融合策略

 提出了一种修正的DS 证据融合策略,以解决证据冲突情况下的融合问题,该方法综合利用了传统DS 证据理论方法所具有的收敛性好及加性融合方法所具有的可靠性高两方面的优点。该方法先对DS 证据理论的结果与加性策略结果之间的一致性做出判断,如果一致性好,则采用DS 证据理论的结果作为最终结果,这样就保证了算法收敛性,否则,用加性策略的结果对DS 证据理论的结果进行修正,从而保证算法的可靠性。该方法在解决冲突证据的合成问题方面体现出良好的适应能力。     

判断矩阵的一个恰当的校正方法

 本文首先证明了成对比较矩阵在相容性矩阵集合中的最佳逼近的存在性和不唯一性。然后通过微分同胚,把原来的非线性逼近转化成一个线性逼近,从而用投影定理获得解决。算例证明该方法简易可行。     

状态空间气动弹性模型及其在颤振计算中的应用

本文评述了已有的状态空间气动弹性模型,并对它们提出了一些改进意见。本文还提出了一种新的状态空间气动弹性模型。用两种机翼为例进行了颤振计算。算例表明:这种新的状态空间气动弹性模型是一种精度较高而阶数较低的模型。本文还提出了用状态空间气动弹性模型进行颤振计算时自动识别模态及自动确定颤振点的方法。     

基于D-S证据理论的目标属性辨识研究

D—S证据组合理论已经成为不确定性推理的一种重要方法，基于该理论的多传感器决策层信息融合已得到广泛应用。本文在简要介绍D—S证据组合理论框架基础上，阐述了基于D—S证据组合理论的信息融合一般步骤及决策层判决方法；分析了发生证据严重冲突时，Dempster证据组合规则出现的不可靠和不合理的本质原因，介绍了针对D—S证据组合规则进行修正的几种主要方法；最后，提出了需要继续深入探讨的问题。     

一种改进的运载火箭迭代制导方法

为提高运载火箭在大气层外当推力出现故障条件下制导算法的最优性、鲁棒性和适应性,提出了一种改进的迭代制导方法。该方法以基于最优控制理论推导的解析形式作为最优控制解,并推导出以5个轨道根数作为终端约束的横截条件,增强了算法的最优性;迭代过程中采用高斯-勒让德方法计算推力积分,并采用球极坐标系下泰勒多项式逼近方法计算引力积分,提高了故障模式下算法的积分精度;该算法采用降维迭代求解模式,并结合对控制变量的合理限幅,保障了推力故障条件下算法的实时性和收敛性。分别基于蒙特卡洛打靶和推力故障条件下进行仿真验证,结果验证了所提方法具有较强的最优性、鲁棒性和故障适应能力。     

高度修正实现刚度等效的梁结构有限元建模方法

民用飞机中对于梁结构的有限元建模,一般采用刚度等效方法将梁建成杆单元与剪切板单元的组合形式,这种有限元简化方法仅能保证截面刚度与真实截面刚度近似等效,但有限元中的截面面积与实际面积却相差较大,致使有限元分析的结果不能准确反映结构严重部位的应力情况。针对传统刚度等效有限元建模方法的不足,提出一种采用高度修正的刚度等效有限元建模方法;将该方法的计算结果与传统建模方法的计算结果、理论计算结果、试验测量结果进行对比。结果表明：本文提出的高度修正的有限元建模方法比传统杆板系建模方法更准确,与试验结果更接近。将该方法用于民用飞机结构有限元建模,能够对真实结构进行更准确地模拟,分析结果也更精确,对民用飞机结构的强度分析和安全保障具有重要的意义。