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A self-learning refined model and tracking for near space hypersonic vehicle by space-based radar 认领 引用 被引量：1
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作者 Yue XU Quan PAN +2 位作者 Zengfu WANG Hua LAN Shuling JIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期504-529,共26页
The Near Space Hypersonic Vehicle(NSHV)features a unique design and propulsion system,achieving exceptional speed,range,and maneuverability,which challenge ground-based radars.Space-Based Radar(SBR)offers a breakthrou... The Near Space Hypersonic Vehicle(NSHV)features a unique design and propulsion system,achieving exceptional speed,range,and maneuverability,which challenge ground-based radars.Space-Based Radar(SBR)offers a breakthrough for tracking NSHV targets,with allweather operation and freedom from Earth's curvature,but faces complex coordinate transformations.Traditional models often overlook the NSHV's dynamic gliding trajectory,especially the impact of hidden control variables on maneuvering,causing mismatches during rapid motion changes.This paper proposes a refined tracking model unified in the ECEF coordinate frame,incorporating model parameters that implicitly encode control laws,and presents an ExpectationMaximization Multi-swarm Cooperative Particle Swarm Optimization(EM-MCPSO)framework for both NSHV tracking and model parameter estimation to address this problem.To minimize conversion errors,a transformation matrix directly represented by the state in the EarthCentered Earth-Fixed(ECEF)coordinate is derived.Then the hybrid aerodynamic acceleration coefficients are introduced to precisely describe the dynamic behaviors,formulating target tracking as a joint estimation problem of state and parameters within EM framework.Finally,a self-learning algorithm based on a master–slave structured PSO is proposed to solve the optimization of the conditional expectations of EM under strong nonlinearity,with a Proportional-Derivative(PD)controller accelerating convergence,and updating the population structure with historical data.Simulations of vertical gliding and horizontal maneuvers validate the algorithm's effectiveness. 展开更多
关键词 Dynamics modeling Expectation Maximization(EM) Maneuvering target tracking Near Space Hypersonic Vehicle(NSHV) Particle Swarm Optimization(PSO)
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基于EM算法的幂-均匀混合分布参数估计 认领 引用
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作者 赵志文 苑洋 杨凯 《沈阳师范大学学报（自然科学版）》 CAS 2026年第1期81-89,共9页
对于有偏态长尾和均匀分散混合特征的数据来说,单一的幂分布或均匀分布很难对此类数据进行拟合,因而构建幂-均匀混合分布,该混合分布的期望、方差等数字特征与单一的幂分布或均匀分布不同,能够较好地刻画偏态长尾与均匀分散共存这一数... 对于有偏态长尾和均匀分散混合特征的数据来说,单一的幂分布或均匀分布很难对此类数据进行拟合,因而构建幂-均匀混合分布,该混合分布的期望、方差等数字特征与单一的幂分布或均匀分布不同,能够较好地刻画偏态长尾与均匀分散共存这一数据特征。基于期望极大化(expectation-maximization,EM)算法,利用极大似然估计方法对模型参数进行估计,并通过数值模拟实验验证了有限样本下估计方法的可行性。此外,将此混合模型应用在实际数据的建模拟合中,与单一分布的拟合效果进行比较,结果显示所提出的混合模型具有更小的拟合偏差。 展开更多
关键词 幂分布 均匀分布 混合模型 极大似然估计 期望极大化算法
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多偏斜量测下异构无人机群EM自适应跟踪方法 认领 引用
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作者 马天力 李红 +2 位作者 石沛灵 陈超波 王可鑫 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2026年第8期2729-2737,共9页
异构无人机(UAV)群凭借功能互补、能力协同的优势,成为对空防御的主要威胁,该类目标群整体轮廓多呈非凸形,且内部子目标在尺寸及结构上存在差异,使得量测呈现多偏斜分布特性,但现有群目标跟踪方法多建立在目标凸形及量测均匀分布假设上... 异构无人机(UAV)群凭借功能互补、能力协同的优势,成为对空防御的主要威胁,该类目标群整体轮廓多呈非凸形,且内部子目标在尺寸及结构上存在差异,使得量测呈现多偏斜分布特性,但现有群目标跟踪方法多建立在目标凸形及量测均匀分布假设上,一旦实际场景与假设条件不符,将导致跟踪性能下降甚至出现跟踪失败。因此,针对具有非凸形轮廓与非均匀量测分布下的异构无人机群运动状态与扩展形态的估计问题,提出一种多偏斜量测下异构无人机群期望最大化(EM)自适应跟踪方法。建立多偏斜量测噪声表示模型,利用EM理论对异构群目标中子群数目和量测分布参数进行辨识;对每个异构子群目标运动状态、扩展形态及量测噪声参数运用变分贝叶斯推理策略进行在线估计;通过对多个异构子群目标扩展形态进行并集求解,获得异构群目标的运动状态和非凸扩展形态。实验结果表明:相较于基于随机矩阵模型(RMM)、随机超曲面模型(RHM)、多椭圆模型(MEM)的群目标跟踪策略及基于偏斜正态的变分贝叶斯跟踪方法,所提方法对于具有非凸形轮廓且量测非均匀分布的异构无人机群运动状态与扩展形态具有更高的估计精度。 展开更多
关键词 异构无人机群 多偏斜分布 非凸扩展形态 期望最大化算法 变分贝叶斯推理
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基于TAN-EM的在役桥梁事故风险致因分析 认领 引用
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作者 申建红 郭明慧 +1 位作者 王涵 王硕 《哈尔滨商业大学学报（自然科学版）》 CAS 2026年第4期480-488,共9页
随着我国桥梁逐步进入老龄化阶段,在役桥梁发生事故的频率不断上升,识别关键风险因素,确保我国在役桥梁的运营安全,对于我国交通基础设施建设具有重要意义.收集了1999~2024年发生的149例在役桥梁事故数据,以客观数据为研究基础,减少了... 随着我国桥梁逐步进入老龄化阶段,在役桥梁发生事故的频率不断上升,识别关键风险因素,确保我国在役桥梁的运营安全,对于我国交通基础设施建设具有重要意义.收集了1999~2024年发生的149例在役桥梁事故数据,以客观数据为研究基础,减少了专家打分造成的主观性.对数据进行处理与风险因素识别.构建了树增强朴素贝叶斯网络(Tree-Augmented Naive Bayes,TAN)模型,突破传统朴素贝叶斯的强独立性假设,显著提升风险识别精度.引入EM(Expectation-Maximization Algorithm,EM)算法进行参数估计并对模型进行优化,解决缺失数据下参数估计偏差问题.利用贝叶斯网络正向推理及反向推理功能,对各类事故的风险因素进行分析,从中挖掘影响在役桥梁事故的关键风险因素,为桥梁安全管理和维护提供科学依据. 展开更多
关键词 在役桥梁事故 风险致因 树增强朴素贝叶斯网络 EM算法 机器学习 桥梁安全
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Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system 认领 引用
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作者 QIN Weiwei GUO Wenxin +2 位作者 HU Chen LIU Gang SONG Tainian 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期769-779,共11页
This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters ... This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method. 展开更多
关键词 human motion capture kinematic calibration exoskeleton gyroscopic drift expectation maximization(EM)
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Parallel Expectation-Maximization Algorithm for Large Databases 认领 引用
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作者 黄浩 宋瀚涛 陆玉昌 《Journal of Beijing Institute of Technology》 EI CAS 2006年第4期420-424,共5页
A new parallel expectation-maximization （EM） algorithm is proposed for large databases. The purpose of the algorithm is to accelerate the operation of the EM algorithm. As a well-known algorithm for estimation in ge... A new parallel expectation-maximization （EM） algorithm is proposed for large databases. The purpose of the algorithm is to accelerate the operation of the EM algorithm. As a well-known algorithm for estimation in generic statistical problems, the EM algorithm has been widely used in many domains. But it often requires significant computational resources. So it is needed to develop more elaborate methods to adapt the databases to a large number of records or large dimensionality. The parallel EM algorithm is based on partial Esteps which has the standard convergence guarantee of EM. The algorithm utilizes fully the advantage of parallel computation. It was confirmed that the algorithm obtains about 2.6 speedups in contrast with the standard EM algorithm through its application to large databases. The running time will decrease near linearly when the number of processors increasing. 展开更多
关键词 expectation-maximization （EM） algorithm incremental EM lazy EM parallel EM
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Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection 认领 引用
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作者 Loka Raj Ghimire Roshan Chitrakar 《Journal of Computer Science Research》 2021年第2期1-10,共10页
Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique ... Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category. 展开更多
关键词 Anomaly detection Clustering EM classification Expectation maximization(EM) Gaussian mixture model(GMM) GMM classification Intrusion detection Naïve Bayes classification
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Novel method for extraction of ship target with overlaps in SAR image via EM algorithm 认领 引用 被引量：2
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作者 CAO Rui WANG Yong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期874-887,共14页
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition... The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method. 展开更多
关键词 expectation maximization(EM)algorithm image processing imaging projection plane(IPP) overlapping ship tar-get synthetic aperture radar(SAR)
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Parameter Estimation of RBF-AR Model Based on the EM-EKF Algorithm 认领 引用 被引量：6
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作者 Yanhui Xi Hui Peng Hong Mo 《自动化学报》 EI CAS CSCD 北大核心 2017年第9期1636-1643,共8页
RBF-AR （radial basis function network-based autoregressive） model is reconstructed as a new type of general radial basis function （RBF） neural network, which has additional linear output weight layer in comparison... RBF-AR （radial basis function network-based autoregressive） model is reconstructed as a new type of general radial basis function （RBF） neural network, which has additional linear output weight layer in comparison with the traditional three-layer RBF network. The extended Kalman filter （EKF） algorithm for RBF training has low filtering accuracy and divergence because of unknown prior knowledge, such as noise covariance and initial states. To overcome the drawback, the expectation maximization （EM） algorithm is used to estimate the covariance matrices of noises and the initial states. The proposed method, called the EM-EKF （expectation-maximization extended Kalman filter） algorithm, which combines the expectation maximization, extended Kalman filtering and smoothing process, is developed to estimate the parameters of the RBF-AR model, the initial conditions and the noise variances simultaneously. It is shown by the simulation tests that the EM-EKF method for the reconstructed RBF-AR network provides better results than structured nonlinear parameter optimization method （SNPOM） and the EKF, especially in low SNR （signal noise ratio）. Moreover, the EM-EKF method can accurately estimate the noise variance. F test indicates there is significant difference between results obtained by the SNPOM and the EM-EKF. 展开更多
关键词 Expectation maximization （EM） algorithm, extended Kalman filtering （EKF） and smoothing, radial basis function（RBF） neural network, RBF-AR model
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DOA estimation and mutual coupling calibration with the SAGE algorithm 认领 引用 被引量：5
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作者 Xiong Kunlai Liu Zhangmeng +1 位作者 Liu Zheng Jiang Wenli 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2014年第6期1538-1543,共6页
In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array ... In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration. 展开更多
关键词 Array self-calibration Convergence Direction of arrival estima-tion Mutual coupling Space alternating generalized expectation-maximization algorithm
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AE-EM:一种期望最大化Web入侵检测算法 认领 引用 被引量：2
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作者 尹兆良 黄于欣 余正涛 《计算机工程与应用》 EI CSCD 北大核心 2025年第3期315-325,共11页
现有的入侵检测算法集中在模式匹配、阈值分割法和多层感知机等机器学习和以神经网络深度学习方法上,在处理基于签名和异常的入侵时效果显著,但耗时费力。在面对Web入侵场景时,现有方法将检测模式重心放在网络流量分析(NTA)上,对URL携... 现有的入侵检测算法集中在模式匹配、阈值分割法和多层感知机等机器学习和以神经网络深度学习方法上,在处理基于签名和异常的入侵时效果显著,但耗时费力。在面对Web入侵场景时,现有方法将检测模式重心放在网络流量分析(NTA)上,对URL携带的负载信息和流量之间的关联语义信息提取不足,异常检测效果有待提升。提出一种无监督算法,名为注意力扩展期望最大化算法(attention expand expectation-maximization algorithm,AE-EM),该算法提取应用层URL中的攻击负载语义,采用Attention机制混合编码网络层流量结构化数据,训练融合多维特征和关联应用层语义的向量作为算法的输入,使用轻量化期望最大化算法估计高斯混合模型的参数,用于网络安全入侵检测的Web入侵检测场景。通过在基线数据集上使用常用的学习算法和消融实验比较,提出的AE-EM算法在Web入侵检测领域准确率和性能上优于传统算法。 展开更多
关键词 入侵检测 Web攻击检测 注意力机制 EM算法 AE-EM算法
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求解多模概率分布Gamma混合模型的半EM算法 认领 引用
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作者 陈佳琪 何玉林 +1 位作者 成英超 黄哲学 《计算机应用》 CSCD 北大核心 2025年第7期2153-2161,共9页
期望最大化(EM)算法在混合模型参数估计中发挥着重要作用,然而现有的EM算法在求解Gamma混合模型(GaMM)参数时存在局限性,主要体现在因近似计算导致的低质量参数估计,以及由于大量数值计算造成的计算效率低下问题。为了克服这些局限,并... 期望最大化(EM)算法在混合模型参数估计中发挥着重要作用,然而现有的EM算法在求解Gamma混合模型(GaMM)参数时存在局限性,主要体现在因近似计算导致的低质量参数估计,以及由于大量数值计算造成的计算效率低下问题。为了克服这些局限,并充分利用数据的多模性质,提出一种半EM(Semi-EM)算法求解用于估计多模概率分布的GaMM。首先,通过聚类探测数据的空间分布特性,以初始化GaMM参数,进而更准确地刻画数据的多模性;其次,在EM算法框架的基础上,对于缺乏封闭更新表达式而导致的参数更新困难问题,采用自定义的启发式策略对GaMM形状参数进行更新,使它们朝着最大化对数似然值的方向逐步调整,同时以封闭形式更新其他参数。经过一系列具有说服力的实验,验证了Semi-EM算法的可行性、合理性和有效性。实验结果表明,Semi-EM算法在精确估计多模概率分布方面优于对比的4种算法,具有更低的误差指标以及更高的对数似然值,表明该算法能提供更准确的模型参数估计,从而更精确地刻画数据的多模性质。 展开更多
关键词 多模概率密度函数 Gamma混合模型 期望最大化算法 聚类 对数似然函数
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NBN-EM模型在建筑基坑施工事故致因分析中的应用 认领 引用
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作者 申建红 孟子祥 +1 位作者 王思冉 张茜 《沈阳大学学报（自然科学版）》 CAS 2025年第3期239-247,共9页
为实现基坑施工不同类型事故的致因分析,从源头遏制基坑施工事故的发生,为事故相关方的风险预防和控制提供决策支持,首先,在收集2008—2023年的200份基坑施工事故报告的基础上,利用扎根理论对基坑施工事故进行3级编码,识别出基坑施工风... 为实现基坑施工不同类型事故的致因分析,从源头遏制基坑施工事故的发生,为事故相关方的风险预防和控制提供决策支持,首先,在收集2008—2023年的200份基坑施工事故报告的基础上,利用扎根理论对基坑施工事故进行3级编码,识别出基坑施工风险的致因因素;其次,采用改进的朴素贝叶斯网络的拓扑结构和EM算法,使用GeNIE软件对200份基坑施工事故数据进行训练,得到基坑施工事故致因分析的朴素贝叶斯网络模型;最后,通过概率分析和敏感性分析对不同类型事故致因因素的重要度进行排序,得到不同类型事故的关键致因因素。同时,一方面通过模型验证,得出模型的准确率为82.5%,验证了模型的可行性;另一方面通过情景分析,预测在不同风险因素组合下最可能发生的基坑施工事故类型,为事故相关方的风险预测以及采取防控措施提供理论支撑。 展开更多
关键词 基坑施工事故 致因分析 数据驱动 EM算法 朴素贝叶斯
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Noise estimation and filtering method of MEMS gyroscope based on EMMAP 认领 引用 被引量：1
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作者 CHEN Guangwu YU Yue +1 位作者 LI Wenyuan LIU Hao 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第2期170-176,共7页
Aiming at the problems of low measurement accuracy,uncertainty and nonlinearity of random noise of the micro electro mechanical system(MEMS)gyroscope,a gyroscope noise estimation and filtering method is proposed,which... Aiming at the problems of low measurement accuracy,uncertainty and nonlinearity of random noise of the micro electro mechanical system(MEMS)gyroscope,a gyroscope noise estimation and filtering method is proposed,which combines expectation maximum(EM)with maximum a posterior(MAP)to form an adpative unscented Kalman filter(UKF),called EMMAP-UKF.According to the MAP estimation principle,a suboptimal unbiased MAP noise statistical estimation model is constructed.Then,EM algorithm is introduced to transform the noise estimation problem into the mathematical expectation maximization problem,which can dynamically adjust the variance of the observed noise.Finally,the estimation and filtering of gyroscope random drift error can be realized.The performance of the gyro noise filtering method is evaluated by Allan variance,and the effectiveness of the method is verified by hardware-in-the-loop simulation. 展开更多
关键词 micro electro mechanical system(MemS)gyroscope expectation maximization(EM)algorithm noise estimation unscented Kalman filter(UKF)
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Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model 认领 引用
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作者 Patience Isiaho Daisy Salifu +1 位作者 Samuel Mwalili Henri E. Z. Tonnang 《Journal of Data Analysis and Information Processing》 2024年第1期24-39,共16页
Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent s... Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data. 展开更多
关键词 Mixture Cure Models Clustered Survival Data Correlation Structure Cox-Snell Residuals EM Algorithm Expectation-Solution Algorithm
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Performances of Chaos Coded Modulation Schemes Based on Mod-MAP Mapping and High Dimensional LDPC Based Mod-MAP Mapping with Belief Propagation 认领 引用
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作者 Naim Khodor Jean-pierre Cances +1 位作者 Vahid Meghdadi Raymond Quere 《International Journal of Communications, Network and System Sciences》 2010年第6期495-506,共12页
In this paper, we propose to generalize the coding schemes first proposed by Kozic &amp;amp;amp;al to high spectral efficient modulation schemes. We study at first Chaos Coded Modulation based on the use of small ... In this paper, we propose to generalize the coding schemes first proposed by Kozic &amp;amp;amp;al to high spectral efficient modulation schemes. We study at first Chaos Coded Modulation based on the use of small dimensional modulo-MAP encoding process and we give a solution to study the distance spectrum of such coding schemes to accurately predict their performances. However, the obtained performances are quite poor. To improve them, we use then a high dimensional modulo-MAP mapping process similar to the low-density generator-matrix codes (LDGM) introduced by Kozic &amp;amp;amp;al. The main difference with their work is that we use an encoding and decoding process on GF (2m) which enables to obtain better performances while preserving a quite simple decoding algorithm when we use the Extended Min-Sum (EMS) algorithm of Declercq &amp;amp;amp;Fossorier. 展开更多
关键词 Chaos Coded Modulation Expectation Maximization Gaussian or Rayleigh Mixtures Low-Density Parity-Check (LDPC) Low-Density Generator-Matrix (LDGM) Factor Graph Extended Min-Sum (EMS)
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A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data 认领 引用
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作者 Yisa Adeniyi Abolade Yichuan Zhao 《Open Journal of Modelling and Simulation》 2024年第2期33-42,共10页
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode... Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance. 展开更多
关键词 Compositional Data Linear Regression Model Least Square Method Robust Least Square Method Synthetic Data Aitchison Distance Maximum Likelihood Estimation Expectation-Maximization Algorithm k-Nearest Neighbor and Mean imputation
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基于双高斯分布混合的可解释自适应鲁棒神经网络建模方法 认领 引用
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作者 刘鑫 李琪琪 代伟 《自动化学报》 EI CAS CSCD 北大核心 2026年第3期463-480,共18页
工业过程数据常常受到混合噪声干扰,传统基于单一重尾分布的鲁棒建模方法在处理混合噪声问题时,在准确性与可解释性方面均存在一定局限.基于此,提出一种混合双高斯分布的可解释鲁棒自适应建模方法.该方法首先采用随机配置算法构建基础... 工业过程数据常常受到混合噪声干扰,传统基于单一重尾分布的鲁棒建模方法在处理混合噪声问题时,在准确性与可解释性方面均存在一定局限.基于此,提出一种混合双高斯分布的可解释鲁棒自适应建模方法.该方法首先采用随机配置算法构建基础的随机配置网络学习模型,确定模型的隐含层节点数、输入权重和偏置;其次为保证模型对混合噪声的鲁棒性,构建双高斯分布(一大一小方差)加权组合而成的噪声表征模型;随后利用期望最大化算法自适应迭代学习随机配置网络输出权值和混合高斯模型噪声参数,最终形成基于双高斯分布混合鲁棒建模方法.该方法具有以下优势:噪声模型能够通过参数自适应学习逼近实际混合噪声特性,其中大方差高斯分量负责对异常噪声进行粗调,小方差高斯分量则用于精细拟合主体噪声,从而增强模型的可解释性;在网络模型输出权值估计过程中,通过为每个输出数据点自适应分配惩罚权重,保障模型的鲁棒性能.为验证所提方法的有效性,分别在函数仿真、基准数据集和工业实例上设计多组对比实验,结果均表明所提方法具备良好的可靠性与实用性. 展开更多
关键词 随机配置网络 双高斯分布混合 鲁棒建模方法 期望最大化算法
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Real-time reliability evaluation based on damaged measurement degradation data 认领 引用 被引量：18
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作者 王小林 蒋平 +1 位作者 郭波 程志君 《Journal of Central South University》 SCIE EI CAS 2012年第11期3162-3169,共8页
A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not... A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product's performance.However,in some cases,the measurement process may exert extra stress on products being measured.To obtain trustful results in such a situation,a new degradation model was derived.Then,by fusing the prior information of product and its own on-line degradation data,the real-time reliability was evaluated on the basis of Bayesian formula.To make the proposed method more practical,a procedure based on expectation maximization (EM) algorithm was presented to estimate the unknown parameters.Finally,the performance of the proposed method was illustrated by a simulation study.The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results,if the damaged measurement process is involved. 展开更多
关键词 degradation analysis damaged measurement real-time reliability expectation maximization algorithm
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EM算法在Wiener过程随机参数的超参数值估计中的应用 认领 引用 被引量：20
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作者 徐廷学 王浩伟 张鑫 《系统工程与电子技术》 EI CSCD 北大核心 2015年第3期707-712,共6页
Wiener过程广泛用于产品的性能退化建模,为了便于Bayesian统计推断大都采用随机参数的共轭先验分布。针对目前的二步法得到的超参数先验估计值精度不高的问题,研究了最大期望(expectation maximization,EM)算法在Wiener过程超参数先验... Wiener过程广泛用于产品的性能退化建模,为了便于Bayesian统计推断大都采用随机参数的共轭先验分布。针对目前的二步法得到的超参数先验估计值精度不高的问题,研究了最大期望(expectation maximization,EM)算法在Wiener过程超参数先验估计中的应用。EM算法将随机参数作为隐含变量对先验信息进行整体处理,利用随机参数的期望值代替其估计值,通过Expectation和Maximization组成的递归迭代过程寻找超参数的估计值。仿真实验表明,EM算法相比于二步法提高了估计精度,特别是在采样数量较少时EM算法具有较大的精度优势。GaAs激光器实例应用表明EM算法不但具备很好的收敛性而且有良好的工程应用价值。 展开更多
关键词 可靠性 最大期望算法 Wiener过程 共轭先验分布 超参数
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