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NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS 被引量:6
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作者 Yan Weiwu Shao HuiheDepartment of Automation,Shanghai Jiaotong University,Shanghai 200030, China 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2003年第2期117-119,共3页
In the industrial process situation, principal component analysis (PCA) is ageneral method in data reconciliation. However, PCA sometime is unfeasible to nonlinear featureanalysis and limited in application to nonline... In the industrial process situation, principal component analysis (PCA) is ageneral method in data reconciliation. However, PCA sometime is unfeasible to nonlinear featureanalysis and limited in application to nonlinear industrial process. Kernel PCA (KPCA) is extensionof PCA and can be used for nonlinear feature analysis. A nonlinear data reconciliation method basedon KPCA is proposed. The basic idea of this method is that firstly original data are mapped to highdimensional feature space by nonlinear function, and PCA is implemented in the feature space. Thennonlinear feature analysis is implemented and data are reconstructed by using the kernel. The datareconciliation method based on KPCA is applied to ternary distillation column. Simulation resultsshow that this method can filter the noise in measurements of nonlinear process and reconciliateddata can represent the true information of nonlinear process. 展开更多
关键词 principal component analysis KERNEL data reconciliation nonlinear
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Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis 被引量:1
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作者 王莹 曾平 +1 位作者 罗雪梅 谢琨 《Journal of Southeast University(English Edition)》 EI CAS 2009年第4期486-490,共5页
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis (PCA)to reduce the dimensions of the multi-spectral data, a nonne... In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis (PCA)to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA. 展开更多
关键词 spectral color science nonnegative constrained principal component analysis low-dimensional spectral space nonlinear optimization multi-spectral images spectral reflectance
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Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis 被引量:23
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作者 ZHANG Ying-Wei ZHOU Hong QIN S. Joe 《自动化学报》 EI CSCD 北大核心 2010年第4期593-597,共5页
关键词 分散系统 MBKPCA SPF PCA
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Nonlinear Dynamic Analysis of MPEG-4 Video Traffic
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作者 GE Fei CAO Yang WANG Yuan-ni 《Wuhan University Journal of Natural Sciences》 EI CAS 2005年第6期1019-1024,共6页
The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The p... The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics. 展开更多
关键词 MPEG-4 video traffic behavior nonlinear dynamic analysis power spectral density principal components analysis correlation dimension largest Lyapunov exponent
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基于NLPCA-GSO可持续发展评价——以环渤海区域为例 被引量:4
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作者 檀菲菲 陆兆华 《生态学报》 CAS CSCD 北大核心 2016年第8期2403-2412,共10页
区域可持续发展水平、发展的持续性和系统的协调性是区域可持续发展定量评价研究的三角构架,而在传统上基于各子系统主成分分析结果直接进行形色各异的加权计算对可持续发展评价而言是有待商榷的。提出了非线性主成分分析和施密特正交化... 区域可持续发展水平、发展的持续性和系统的协调性是区域可持续发展定量评价研究的三角构架,而在传统上基于各子系统主成分分析结果直接进行形色各异的加权计算对可持续发展评价而言是有待商榷的。提出了非线性主成分分析和施密特正交化(NLPCA-GSO)相耦合的方法评价区域的可持续发展水平来弥补传统方法的不足,并由此建立区域发展持续性模型和可持续发展系统协调度模型,再以环渤海区域为实证分析其2001—2010年的可持续发展状况。结果表明:基于NLPCA-GSO的可持续发展水平模型可以很好地弥补传统主成分分析及对各子系统结果的综合评价的不足;区域发展持续性模型、协调性模型和区域可持续系统变化的滤波分析形象地揭示区域可持续发展的实质和内涵;实证研究表明环渤海区域在研究时段内可持续发展水平有所上升,而环境子系统持续性的下降是引起区域发展持续性和系统协调度的变化的主要原因。研究结果可丰富区域可持续发展评价的方法学,也可为环渤海区域的可持续发展研究奠定基础。 展开更多
关键词 非线性主成分分析 施密特正交化 可持续发展评价 环渤海区域
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基于双层自适应集成残差主成分分析的复杂非线性过程监测
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作者 唐徐佳 卢伟鹏 颜学峰 《华东理工大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第1期88-96,共9页
多元统计监测方法常使用正常数据选取特征,而现实过程中,不同的故障将影响不同的特征,并且这些特征可能随着时间和控制系统的作用而变化。当故障发生并随时间变化时,要想获得更好的故障检测能力,就需要聚集有效的故障敏感特征。本文提... 多元统计监测方法常使用正常数据选取特征,而现实过程中,不同的故障将影响不同的特征,并且这些特征可能随着时间和控制系统的作用而变化。当故障发生并随时间变化时,要想获得更好的故障检测能力,就需要聚集有效的故障敏感特征。本文提出了一种双层自适应集成残差主成分分析(AERPCA)模型,其子模型包含不同的特征,并突出地呈现一个或多个相关故障。首先,根据正常数据计算主成分分析(PCA)特征,利用不同特征构建线性子模型和相应的残差空间。考虑到残差空间的非线性特性及有效特征更为分散,采用核PCA(KPCA)提取不同的特征并组成同一残差空间下不同KPCA子模型。然后,利用贝叶斯方法获取集成KPCA子模型,完成各残差空间的划分和集成。最后,在主空间中获得多个线性子模型以及在残差空间中获得多个集成的非线性子模型后,利用滑动窗口确定当前时刻监控效果最好的模型。采用田纳西-伊士曼过程验证了AERPCA的有效性。 展开更多
关键词 集成学习 自适应过程 核主成分分析 非线性过程监测 故障诊断
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基于NLPCA-RBF神经网络的番茄蒸散量预测 被引量:1
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作者 陆林 焦俊 +3 位作者 汪宏喜 陈袆琼 张兆义 鲁威 《中国农学通报》 CSCD 2014年第11期134-139,共6页
蒸散量(ET)是水文循环中的重要组成部分。精确的ET预测在水资源管理和灌溉系统设计等方面的研究是十分必要的。利用非线性主成分分析法(NLPCA)和径向基(RBF)神经网络组成的模型(NLPCA-RBF)对番茄蒸散量进行估算。在既保证ET影响因素信... 蒸散量(ET)是水文循环中的重要组成部分。精确的ET预测在水资源管理和灌溉系统设计等方面的研究是十分必要的。利用非线性主成分分析法(NLPCA)和径向基(RBF)神经网络组成的模型(NLPCA-RBF)对番茄蒸散量进行估算。在既保证ET影响因素信息完整,又可消除影响因素之间相关性的前提下,利用NLPCA将影响ET的7个气象因素简化为3个综合成分,并以此为网络训练的输入数据,根据实测的蒸散量作为网络输出建立了RBF神经网络,并且经非训练样本点数据检验。结果表明,与传统RBF网络模型较,NLPCA-RBF网络预测模型能够更好的反应影响因子与蒸散量之间的关系,取得更为精确的结果。 展开更多
关键词 蒸散量 非线性主成分分析 RBF神经网络 估算
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结合逆非线性主成分分析和极值理论的桥梁损伤检测
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作者 刘迅 卓卫东 林楷奇 《福州大学学报(自然科学版)》 CAS 北大核心 2024年第3期345-352,共8页
为提高环境和运营变化(environmental and operational variations,EOV)影响下的桥梁损伤检测可靠性,结合逆非线性主成分分析(inverse nonlinear principal component analysis,INLPCA)和极值理论,提出一种新的桥梁损伤检测方法.该方法... 为提高环境和运营变化(environmental and operational variations,EOV)影响下的桥梁损伤检测可靠性,结合逆非线性主成分分析(inverse nonlinear principal component analysis,INLPCA)和极值理论,提出一种新的桥梁损伤检测方法.该方法采用INLPCA对桥梁损伤特征进行建模,利用不完备健康监测数据的估计均方误差和添加神经网络训练惩罚项控制INLPCA的非线性程度.采用INLPCA对损伤特征的重构误差和马氏平方距离(Mahalanobis squared distance,MSD)建立损伤指标(ID),最后基于ID的广义极值(generalized extreme value,GEV)分布建立损伤检测阈值.以比利时KW51铁路桥和天津永和斜拉桥为例,验证所提方法的有效性.结果表明,所提方法能准确检测EOV影响下的桥梁损伤,且对不同桥型和不同损伤特征均有良好的适用性. 展开更多
关键词 桥梁结构 损伤检测 健康监测 环境和运营变化(EVO) 逆非线性主成分分析(Inlpca) 极值理论
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基于NLPCA的聚类可视化方法
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作者 齐志 李季 赵晓丹 《吉林大学学报(信息科学版)》 CAS 2010年第5期526-532,共7页
为了将高维输入空间的数据映射到低维空间,利用可视化技术探测数据的固有特性,提出了用非线性主成分分析(NLPCA:NonLinear Principal Component Analysis)和自组织映射网络相结合的方法对生物信息学中基因表达数据进行聚类可视化分析。... 为了将高维输入空间的数据映射到低维空间,利用可视化技术探测数据的固有特性,提出了用非线性主成分分析(NLPCA:NonLinear Principal Component Analysis)和自组织映射网络相结合的方法对生物信息学中基因表达数据进行聚类可视化分析。实验结果表明,该方法有较高的分类正确率,用于基因表达数据的聚类分析是行之有效的。 展开更多
关键词 自组织特征映射神经网络 非线性主成分分析 聚类分析 可视化
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2020—2022年咸宁市臭氧污染气象特征及成因分析
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作者 乐东明 王文浚 +4 位作者 王颖 高欢 郑进平 谭志农 傅尧 《黑龙江环境通报》 2024年第5期30-32,共3页
气象条件对臭氧的形成、转化和扩散有着重要的影响。本文重点研究了2020—2022年咸宁市气象条件与臭氧污染的相关性,通过主成分分析及非线性拟合对O3成因进行了进一步分析。主成分分析结果表明咸宁市臭氧污染受本地光化学反应影响较大,... 气象条件对臭氧的形成、转化和扩散有着重要的影响。本文重点研究了2020—2022年咸宁市气象条件与臭氧污染的相关性,通过主成分分析及非线性拟合对O3成因进行了进一步分析。主成分分析结果表明咸宁市臭氧污染受本地光化学反应影响较大,其中对臭氧浓度影响最大的气象因子是温度和湿度。非线性拟合结果表明温度相较湿度对臭氧浓度的影响更大,当温度处于29~31℃时,湿度处于45%~60%时,臭氧浓度及其超标率会大幅增加。研究结果为咸宁市臭氧污染预报和防治提供了较为客观、有效的参考依据。 展开更多
关键词 臭氧 温度 湿度 主成分分析 非线性拟合
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Multivariate Statistical Process Monitoring Using Robust Nonlinear Principal Component Analysis 被引量:6
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作者 赵仕健 徐用懋 《Tsinghua Science and Technology》 SCIE EI CAS 2005年第5期582-586,共5页
The principal component analysis (PCA) algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the non... The principal component analysis (PCA) algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring (MSPM) scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes. 展开更多
关键词 robust nonlinear principal component analysis autoassociative networks multivariate statisticaprocess monitoring (MSPM) fluidized catalytic cracking unit (FCCU)
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Taiwan’ Chi-Chi Earthquake Precursor Detection Using Nonlinear Principal Component Analysis to Multi-Channel Total Electron Content Records 被引量:2
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作者 Jyh-Woei Lin 《Journal of Earth Science》 SCIE CAS CSCD 2013年第2期244-253,共10页
This research uses eigenvalue characteristics of nonlinear principal component analysis (NLPCA) and principal component analysis (PCA) to investigate total electron content (TEC) anomalies associated with Taiwan... This research uses eigenvalue characteristics of nonlinear principal component analysis (NLPCA) and principal component analysis (PCA) to investigate total electron content (TEC) anomalies associated with Taiwan's Chi-Chi earthquake of 21 September 1999 (LT) (M_w=7.6). The transforms are used for ionospheric TEC from 01 August to 20 September 1999 (local time) using data from 13 GPS receivers. The data were collected at 22°N-26°N Lat. and 120°E-122°E Long.. Applying the NLPCA to the multi-channel total electron content records of GPS receivers, the earthquake-associated TEC anomalies were represented by large principal eigenvalues of NLPCA (〉0.5 in a normalized set) on 14 August and 17, 18, and 20 September, with allowance given for the Dst index, which was quiet for the study period. Comparisons were then made with other researchers who also found TEC anomalies on September 17, 18, and 19 associated with the Chi-Chi earthquake, which cannot be detected by PCA.Consideration is also given for reported ground level geomagnetic field activity that occurred between mid-August and late October, leading up to and including the Chi-Chi and Chia-Yi earthquakes, which are associated with the same series of faults. It is possible that Aug. 14 is representative of an earthquake-associated TEC anomaly. This is an interesting result given how much earlier than the earthquake it occurred. 展开更多
关键词 nonlinear principal component analysis principal component analysis multi-channel total electron content records Taiwan's Chi-Chi earthquake
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Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Pro cesses 被引量:12
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作者 PENG Kai-Xiang ZHANG Kai LI Gang 《自动化学报》 EI CSCD 北大核心 2014年第3期423-430,共8页
在过去的十年,核主管部件分析(KPCA ) 在监视区域的数据驱动的过程相当流行地出现了。庞大的工作被做了显示出它的简洁,可行性,和有效性。然而,核诡计的介绍使直接为差错诊断采用传统的贡献阴谋不可能。在这份报纸,根据重游并且分... 在过去的十年,核主管部件分析(KPCA ) 在监视区域的数据驱动的过程相当流行地出现了。庞大的工作被做了显示出它的简洁,可行性,和有效性。然而,核诡计的介绍使直接为差错诊断采用传统的贡献阴谋不可能。在这份报纸,根据重游并且分析存在, KPCA 相关的诊断来临,新贡献率基于方法被建议它能清楚地解释有缺点的变量。而且,为联机非线性的诊断的一个计划被建立。最后,连续搅动的坦克反应堆(CSTR ) 上的案例研究基准被使用存取新方法论的有效性,在有传统的线性方法的比较也被包含的地方。 展开更多
关键词 故障诊断 非线性 搅拌釜式反应器 工业 费率 核主成分分析 KPCA 数据驱动
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A nonlinear PCA algorithm based on RBF neural networks 被引量:1
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作者 杨斌 朱仲英 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第1期101-104,共4页
Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal com... Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction. 展开更多
关键词 principal component analysis (PCA) nonlinear PCA (nlpca) Radial Basis Function (RBF) neural network Orthogonal Least Squares (OLS)
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Nonlinear Principal Component Analysis Using Strong Tracking Filter
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作者 丁子哲 张贤达 朱孝龙 《Tsinghua Science and Technology》 SCIE EI CAS 2007年第6期652-657,共6页
The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which... The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm. 展开更多
关键词 nonlinear principal component analysis strong tracking filter recursive least-squares
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基于Sentinel-2B的油松冠层可燃物含水率反演研究 被引量:1
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作者 刘鸿升 欧阳文欣 +2 位作者 魏英杰 谢亦秋 李建军 《林业资源管理》 北大核心 2023年第4期141-149,共9页
森林火灾的发生与植被冠层可燃物含水率的大小有着密切联系。利用高精度、大尺度、高效率的遥感影像反演获取植被冠层可燃物含水率对于有效防治森林火灾具有重要意义。油松由于其自身理化性质成为引发森林火灾的主要树种之一,以张家口... 森林火灾的发生与植被冠层可燃物含水率的大小有着密切联系。利用高精度、大尺度、高效率的遥感影像反演获取植被冠层可燃物含水率对于有效防治森林火灾具有重要意义。油松由于其自身理化性质成为引发森林火灾的主要树种之一,以张家口崇礼区的油松为研究对象,基于Sentinel-2B遥感影像和油松含水率实测数据,建立了多个油松冠层可燃物含水率反演模型:一元线性回归模型、一元非线性回归模型和多元非线性回归模型,并利用决定系数(R 2)和均方根误差(RMSE)进行模型精度评价。结果表明,非线性模型总体上要优于线性模型;通过多个自变量因子建立的多元非线性模型能够更好地反映油松冠层可燃物含水率情况,模型反演精度更高,可以为植被冠层可燃物含水率反演模型方法选择提供一定的理论依据。 展开更多
关键词 Sentinel-2B 油松 冠层可燃物含水率 线性回归 多元非线性回归
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基于KPCA和SSA优化SVM的非线性过程故障检测 被引量:2
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作者 申志 李元 《计算机与现代化》 2023年第6期15-20,32,共7页
针对工业过程产生的非线性数据存在特征维数高的问题,提出一种基于核主成分分析(Kernel Principal Compo⁃nent Analysis,KPCA)和麻雀搜索算法(Sparrow Search Algorithm,SSA)优化支持向量机(Support Vector Machine,SVM)相结合的过程故... 针对工业过程产生的非线性数据存在特征维数高的问题,提出一种基于核主成分分析(Kernel Principal Compo⁃nent Analysis,KPCA)和麻雀搜索算法(Sparrow Search Algorithm,SSA)优化支持向量机(Support Vector Machine,SVM)相结合的过程故障检测算法。首先,采用KPCA算法提取工业过程数据的线性和非线性特征。然后,将提取特征后的数据作为训练样本建立SVM模型,同时利用SSA算法对SVM的惩罚因子和核参数进行优化,寻找最佳分类模型。最后,将最佳的分类模型应用于测试样本进行故障检测。为了验证所提算法的分类效果,本文利用田纳西伊斯曼化工过程数据,将KPCA-SSA-SVM与SVM、KPCA-GA-SVM(Genetic Algorithm,GA)进行对比分析,验证了所提算法的高效性和优越性。 展开更多
关键词 核主成分分析 麻雀搜索算法 支持向量机 非线性过程 故障检测
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基于多维特征与IGWO-SVM的电机轴承故障诊断 被引量:2
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作者 张涛 王朝阳 +2 位作者 吴鑫辉 葛平淑 王阳 《兵器装备工程学报》 CAS CSCD 北大核心 2023年第9期149-154,210,共7页
针对电机轴承故障诊断精度低、传统灰狼优化算法(GWO)优化支持向量机(SVM)故障诊断模型容易陷入局部最优的问题,引入非线性收敛因子和Levy飞行策略对改进灰狼优化算法(IGWO)进行研究,提出了一种基于多维特征与改进灰狼优化算法优化支持... 针对电机轴承故障诊断精度低、传统灰狼优化算法(GWO)优化支持向量机(SVM)故障诊断模型容易陷入局部最优的问题,引入非线性收敛因子和Levy飞行策略对改进灰狼优化算法(IGWO)进行研究,提出了一种基于多维特征与改进灰狼优化算法优化支持向量机(IGWO-SVM)的电机轴承故障诊断方法。提取电机轴承振动信号的时域和频域特征构成多维特征矩阵;采用主成分分析(PCA)降低特征矩阵的数据维数,以实现快速数据处理;利用IGWO对SVM模型参数进行优化,得到最优的IGWO-SVM故障诊断模型用于确定电机轴承的故障类型。实验结果表明:所提出的电机轴承故障诊断方法在不同工况下精度高、性能稳定,所提出的IGWO算法与传统GWO和基于差分进化的改进灰狼优化算法(DEGWO)相比,具有更好的收敛性和精度。 展开更多
关键词 电机轴承 主成分分析(PCA) 非线性收敛因子 Levy飞行策略 改进灰狼优化算法(IGWO) 支持向量机(SVM) 故障诊断
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Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes 被引量:1
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作者 Ying-wei ZHANG Yong-dong TENG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2010年第12期948-955,共8页
Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recur... Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables. 展开更多
关键词 Recursive multiblock kernel principal component analysis (RMBPCA) Dynamic process nonlinear process
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Principal component cluster analysis of ECG time series based on Lyapunov exponent spectrum 被引量:4
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作者 WANGNai RUANJiong 《Chinese Science Bulletin》 SCIE EI CAS 2004年第18期1980-1985,共6页
In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-... In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-tabase confirmed the validity of our approach. Another technique named improved teacher selecting student (TSS) algorithm is presented to analyze unknown samples by means of some known ones, which is of better accuracy. This technique combines the advantages of both statistical and nonlinear dynamical methods and is shown to be significant to the analysis of nonlinear ECG time series. 展开更多
关键词 ECG 非线性时间级数分析 李雅普诺夫指数光谱 TSS算法 主要成份聚合分析
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