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
In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different ...In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.展开更多
Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input da...Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.展开更多
Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis ( KPCA ) for an optimal ke...Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis ( KPCA ) for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration (CPSO) is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis (PCA). The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.展开更多
Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte...Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.展开更多
Currently,some fault prognosis technology occasionally has relatively unsatisfied performance especially for incipient faults in nonlinear processes duo to their large time delay and complex internal connection.To ove...Currently,some fault prognosis technology occasionally has relatively unsatisfied performance especially for incipient faults in nonlinear processes duo to their large time delay and complex internal connection.To overcome this deficiency,multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis.In this approach,mutual information estimation and Bayesian information criterion(BIC) are separately used to acquire the correlation degree and time delay of the process variables.Moreover,in order to achieve prediction,time series prediction by back propagation(BP) network is applied whose input is multivariate correlated time series other than the original time series.Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis(LKPCA) model for incipient fault prognosis.The new method has been exemplified in a simple nonlinear process and the complicated Tennessee Eastman(TE) benchmark process.The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods.展开更多
Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful ...Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).展开更多
为了实现非侵入式负荷监测的功能并提高负荷辨识准确率,文中提出一种基于机器学习的负荷辨识方法。在家用电器电流波形及各次谐波特征的数据中,采用核主成分分析方法(Kernel Principal Components Analysis,KPCA),解决非线性特征提取与...为了实现非侵入式负荷监测的功能并提高负荷辨识准确率,文中提出一种基于机器学习的负荷辨识方法。在家用电器电流波形及各次谐波特征的数据中,采用核主成分分析方法(Kernel Principal Components Analysis,KPCA),解决非线性特征提取与降维的问题,最大限度抽取特征信息。再利用一维卷积核提取时序特征并压缩后输入到XGBoost模型,得到负荷辨识结果。通过在实验室中采集数据进行算法验证,文中提出算法在各类用电器的识别中均具有较高的准确率。展开更多
Kernal factor analysis (KFA) with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle com...Kernal factor analysis (KFA) with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition.展开更多
基金This project is supported by Special Foundation for Major State Basic Research of China (Project 973, No.G1998030415)
文摘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.
基金supported by Zhejiang Provincial Natural Science Foundation of China(LY19F030003)Key Research and Development Project of Zhejiang Province(2021C04030)+1 种基金the National Natural Science Foundation of China(62003306)Educational Commission Research Program of Zhejiang Province(Y202044842)。
文摘In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.
文摘Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.
基金supported by National Natural Science Foundation under Grant No.50875247Shanxi Province Natural Science Foundation under Grant No.2009011026-1
文摘Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis ( KPCA ) for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration (CPSO) is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis (PCA). The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.
基金Supported by the 973 project of China (2013CB733600), the National Natural Science Foundation (21176073), the Doctoral Fund of Ministry of Education (20090074110005), the New Century Excellent Talents in University (NCET-09-0346), "Shu Guang" project (09SG29) and the Fundamental Research Funds for the Central Universities.
基金Climbing Peak Discipline Project of Shanghai Dianji University,China(No.15DFXK02)Hi-Tech Research and Development Programs of China(No.2007AA041600)
文摘Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.
基金Supported by the National Natural Science Foundation of China(61573051,61472021)the Natural Science Foundation of Beijing(4142039)+1 种基金Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2015KF-01)Fundamental Research Funds for the Central Universities(PT1613-05)
文摘Currently,some fault prognosis technology occasionally has relatively unsatisfied performance especially for incipient faults in nonlinear processes duo to their large time delay and complex internal connection.To overcome this deficiency,multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis.In this approach,mutual information estimation and Bayesian information criterion(BIC) are separately used to acquire the correlation degree and time delay of the process variables.Moreover,in order to achieve prediction,time series prediction by back propagation(BP) network is applied whose input is multivariate correlated time series other than the original time series.Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis(LKPCA) model for incipient fault prognosis.The new method has been exemplified in a simple nonlinear process and the complicated Tennessee Eastman(TE) benchmark process.The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods.
文摘Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).
文摘为了实现非侵入式负荷监测的功能并提高负荷辨识准确率,文中提出一种基于机器学习的负荷辨识方法。在家用电器电流波形及各次谐波特征的数据中,采用核主成分分析方法(Kernel Principal Components Analysis,KPCA),解决非线性特征提取与降维的问题,最大限度抽取特征信息。再利用一维卷积核提取时序特征并压缩后输入到XGBoost模型,得到负荷辨识结果。通过在实验室中采集数据进行算法验证,文中提出算法在各类用电器的识别中均具有较高的准确率。
基金The National Defence Foundation of China (No.NEWL51435Qt220401)
文摘Kernal factor analysis (KFA) with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition.