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1.
针对聚丙烯的生产过程是一个大滞后、时变、非线性的复杂系统,提出了基于主成分分析(PCA)的RBF神经网络聚丙烯熔融指数建模方法。该方法用主元分析对高维输入变量进行预处理,构造反应过程信息的低维主元变量,再经径向基函数神经网络对主元变量进行建模。该方法不仅简化了神经网络的结构,而且可以借助主元分析方法对过程故障和过失误差进行侦破,避免导致模型的错误输出。理论分析和实验结果表明,基于PCA和RBF网络方法的聚丙烯熔融指数建模具有精度高、鲁棒性强的优点,有利于工业生产应用。  相似文献   

2.
针对化工过程数据的多尺度性和非线性特性,提出了一种多尺度核主元分析方法(MSKPCA)监控过程的运行状态。使用小波变换在不同尺度下分解测量信号.然后借助于核函数对分解后的数据进行非线性变换,在变换后的线性空间中用主元分析(PCA)提取过程数据的主要特征,构造监控统计量T2和Q来检测故障。在此基础上,提出了一种贡献图方法.计算过程变量对故障的贡献量,用于故障变量的分离。在TE过程上的监控结果表明,MSKPCA可以比PCA和动态PCA更迅速地检测到过程故障,贡献图方法能够正确地分离故障变量。  相似文献   

3.
<正>作为用于描述现实空间的坐标表示方式,人们在复数的基础上创造了四元数并以a+bi+cj+dk的形式说明空间点所在位置。i、j、k作为一种特殊的虚数单位参与运算,并有以下运算规则:i0=j0=k0=1,i2=j2=k2=-1对于i、j、k本身的几何意义可以理解为一种旋转,其中i旋转代表X轴与Y轴相交平面中X轴正向向Y轴正向的旋转,j旋转代表Z轴与X轴相交平面中Z  相似文献   

4.
针对传统的主元分析(PCA)的T~2和平方预测误差(SPE)检验所提供的信息并不一致的缺陷,提出一种改进的PCA方法。该方法采用主元相关变量残差(PVR)和一般变量残差(CVR)统计量代替SPE统计量用于过程监测。将此改进的PCA方法应用到双效蒸发过程的仿真监测,与传统的PCA方法相比,新PCA方法能够有效地识别正常工况改变与过程故障引起的T~2图变化,避免了SPE统计量的保守性,能够提供更详细的过程变化信息,提高了对过程变化的分析与诊断能力。  相似文献   

5.
针对传统的主元分析(PCA)的T~2和平方预测误差(SPE)检验所提供的信息并不一致的缺陷,提出了一种改进的PCA方法。该方法采用主元相关变量残差(PVR)和一般变量残差(CVR)统计量代替SPE统计量用于过程监测。将此改进的PCA方法应用到双效蒸发过程的仿真监测,与传统的PCA方法相比,新PCA方法能够有效地识别正常工况改变与过T~2程故障引起的图变化,避免了SPE统计量的保守性,能够提供更详细的过程变化信息,提高了对过程变化的分析与诊断能力。  相似文献   

6.
主元分析(PCA)是一种能够对过程生产进行监测和质量控制的有效方法,在保证数据信息丢失最少的情况下,大大降低了原始数据空间的维数。为了更好地进行故障检测与诊断,介绍了基于PCA多变量统计的故障检测与诊断,给出了广泛应用在多变量统计过程上的T2和Q(或SPE)统计量。利用PCA分析建模可以消除变量间的非线性关联,降低噪声影响。用田纳西-伊斯曼过程TEP(Tennessee-Eastman Process)平台产生仿真数据,并利用Matlab软件建立故障检测与诊断模型。通过T2和Q(或SPE)统计量与其阈值的判断,进行对系统的故障检测与诊断。实验表明,基于PCA的故障诊断方法能够对过程的非正常变化做出反应,也能较正确地找出发生故障的原因以及相应环节。  相似文献   

7.
基于费舍尔判别分析法的故障诊断   总被引:1,自引:0,他引:1  
在化工流程故障诊断中,主元分析法(PCA)是最常见的降维技术.尽管PCA具有一定的优化性能,并在故障诊断中被广泛使用,却不是故障诊断的最佳方案.理论上,费舍尔判别分析法(FDA)在故障诊断分类方面更具优势.对现实化工厂故障数据进行了研究,得出在低维状态下选择FDA方法可以获得更好的处理效果.  相似文献   

8.
针对传统主元分析法(PCA)应用于复杂非线性的化工过程故障检测时存在性能差的问题,提出利用核主元分析法(KPCA)来进行故障检测的思想.从而将输入空间中复杂的非线性问题转化为特征空间中的线性问题.将上述方法应用于Tennessee Eastman(TE)化工过程模型,仿真结果表明,KPCA方法在复杂非线性化工过程敝障检测方面的应用明显优于普通的PCA方法.  相似文献   

9.
招标信息     
招标项目一:中国机械进出口(集团)有限公司招标传输系统招标行业招标机构截止时间开标地点招标产品名称、数量及主要技术参数招 售价霎 购买时间件 地点联 联系人苎 电话力式 地址 ” , i “ ^ ~ c . v。 一 = ; ”电脑通讯 s囊^~《j 0.j i~壕目名称z 一≯德赣蒸统叠i?i—i。#_蠢ui。“■i_…一i。;?。ji“中国机械进出口(集团)有限公司2001.09-1黟{饿oo:oo。…∥ 玲辩标聪涧= ≯2001一O辨181jlmOOjQQr¨ x“。0‘誊∥j:。。j“鬻0。 、北京市西城区阜外大街1号四川I大厦三层多功能厅A1包售价5500元人民币:A2包售价4000元人民币i A3…  相似文献   

10.
基于回声状态网络的多变量预测模型的研究   总被引:1,自引:0,他引:1  
考虑单变量在混沌时间序列预测中的不足,文章利用多变量模型进行混沌时间序列的预测。针对多变量预测过程中的维数过高问题,文章结合主元分析理论(PCA)和回声状态网络(ESN),构建了基于PCA和ESN的多变量混沌时间序列预测模型,将PCA降维后的时间序列数据输入ESN网络进行预测分析。论文对由Lorenz动态方程生成的三变量混沌时间序列进行了仿真实验,结果表明该模型有效地提高了预测的精度和预测的效率,是一种有效的混沌时间序列预测方法。  相似文献   

11.
Principal component analysis (PCA) is one of the powerful dimension reduction techniques widely used in data mining field. PCA tries to project the data into lower dimensional space while preserving the intrinsic information hidden in the data as much as possible. Disadvantage of PCA is that, extracted principal components (PCs) are linear combination of all features, hence PCs are may still contaminated with noise in the data. To address this problem we propose a modified version of PCA called noise free PCA (NFPCA), in which regularization is introduced during the PCs extraction step to mitigate the effect of noise. Potentials of the proposed method is assessed in two important application of high-dimensional molecular data: classification and survival prediction. Multiple publicly available real-world data sets are used for this illustration. Experimental results show that, the NFPCA produce highly informative than the ordinary PCA method. This is largely due to the fact that the NFPCA suppress the effect of noise in the PCs more efficiently with minimum information lost. The NFPCA is a promising alternative to existing PCA approaches not only in terms of highly informative PCs, but also its relatively cheap computational cost.  相似文献   

12.
基于输入训练神经网络的非线性主元分析(PCA)能够有效地提取过程变量的非线性主元,但是存在主元的个数不能通过网络训练确定,且各个主元重要程度在神经网络中无法区分等缺点,本文提出一种分级输入自调整神经网络,并进一步提出基于此网络的非线性PCA,通过多级输入自调整神经网络,将主元按顺序找出,且根据主元对过程数据的预测误差定量地确定出主元的个数,克服了上述缺点.  相似文献   

13.
主元分析(PCA)在工业生产过程的产品质量控制与故障诊断等方面已得到广泛应用,然而当过程的变量间存在着未知时滞性时,必须确定数据间的对应关系,否则PCA模型将会不准.基于此,提出了PCA优化建模方法.该方法以过程变量间的时滞常数为优化变量,在分析PCA模型特点基础上,确定主成分个数和SPE统计量为综合目标函数,并建立模型约束条件,采用遗传算法求解.最后给出了仿真实例,证明了所提出方法的有效性.  相似文献   

14.
变量的对称性在逻辑综合与优化,工艺映射中起着非常重要的作用。如果事先得到变量对称的信息,就可以减少解空间,提高逻辑验证的效率,过去,人们通常用公式fxix↑-j=fxjx↑-i检验变量的对称性,这需要分别建立fxix↑-j和fxjx↑-i的BDD图,然后检查两BDD图是否同构,文中提出一种新算法,整个算法流程仅需建立一次BDD,任何变量对称性的判别遍历BDD一次即可完成,从而减少了算法的空间复杂度和时间复杂度。  相似文献   

15.
Owing to various manufacturing strategies and demands of markets, chemical processes often involve multiple operating modes. How to identify mode from multimode process data collected under both stable and transitional modes is an important issue. This paper proposes a novel mode identification algorithm-recursive local outlier factor (RLOF) based on the sequential information in the time scale and the density information in the spatial scale. In this algorithm, not only the number of modes does not need to be determined in advance, but also details of mode switching can be acquired. In addition, the principal components (PCs) chosen by the variance of overall dataset in principal component analysis (PCA) cannot guarantee that all variables express information as completely as possible. Using the defined cumulative percent expression (CPE), this study chooses key PCs (KPCs) according to each variable. Moreover, fault diagnosis is realized via the contribution of every variable to key PCs. Finally, the monitoring performance is evaluated under the Tennessee Eastman (TE) benchmark and the continuous stirred tank reactor (CSTR) process.  相似文献   

16.
Principal components analysis (PCA) is probably the best-known approach to unsupervised dimensionality reduction. However, axes of the lower-dimensional space, ie., principal components (PCs), are a set of new variables carrying no clear physical meanings. Thus, interpretation of results obtained in the lower-dimensional PCA space and data acquisition for test samples still involve all of the original measurements. To deal with this problem, we develop two algorithms to link the physically meaningless PCs back to a subset of original measurements. The main idea of the algorithms is to evaluate and select feature subsets based on their capacities to reproduce sample projections on principal axes. The strength of the new algorithms is that the computaion complexity involved is significantly reduced, compared with the data structural similarity-based feature evaluation.  相似文献   

17.
在常减压装置中,影响初馏塔顶石脑油干点的因素很多,反应十分复杂,故难以建立准确的机理模型.针对传统的主元分析法(PCA)或自适应模糊推理系统(ANFIS)建立软测模型中的缺点,本文提出采用主元分析法预处理输入变量,再结合自适应模糊推理系统,进行常减压装置初馏塔顶石脑油干点的软测量模型的改进,能及时测定化工过程的变量,对稳定生产过程,有效控制产品质量具有重要意义.通过MATLAB仿真,表明该改进型方法的软测建模效果较好,建模的训练时间大大节省了,且泛化能力和拟合精度很好.  相似文献   

18.
Principal Component Analysis (PCA) is a well-known linear dimensionality reduction technique in the literature. It extracts global principal components (PCs) and lacks in capturing local variations in its global PCs. To overcome the issues of PCA, Feature Partitioning based PCA (FP-PCA) methods were proposed; they extract local PCs from subpatterns and they are not sensitive to global variations across the subpatterns. Subsequently, SubXPCA was proposed as a novel FP-PCA approach which extracts PCs by utilizing both global and local information; it was proved to be efficient in terms of computational time and classification. It is observed that there is no detailed theoretical investigation done on the properties of FP-PCA methods. Such theoretical analysis is essential to provide generalized and formal validation of the properties of the FP-PCA methods. In this paper, our focus is to show SubXPCA as an alternative to PCA and other FP-PCA methods by proving analytically the various properties of SubXPCA related to summarization of variance, feature orders, and subpattern sizes. We prove analytically that (i) SubXPCA approaches PCA in terms of summarizing variance with increase in number of local principal components of subpatterns; (ii) SubXPCA is robust against feature orders (permutations) of patterns and variety of partitions (subpattern sizes); (iii) SubXPCA shows higher dimensionality reduction as compared to other FP-PCA methods. These properties of SubXPCA are demonstrated empirically upon UCI Waveform and ORL face data sets.  相似文献   

19.
Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretability by approximating sparse PCs whose projections capture the maximal variance of original data. In this paper we present an efficient and paralleled method of SPCA using graphics processing units (GPUs), which can process large blocks of data in parallel. Specifically, we construct parallel implementations of the four optimization formulations of the generalized power method of SPCA (GP-SPCA), one of the most efficient and effective SPCA approaches, on a GPU. The parallel GPU implementation of GP-SPCA (using CUBLAS) is up to eleven times faster than the corresponding CPU implementation (using CBLAS), and up to 107 times faster than a MatLab implementation. Extensive comparative experiments in several real-world datasets confirm that SPCA offers a practical advantage.  相似文献   

20.
Nowadays the emergency decision has become a hot topic in the field of decision-making with interval-valued Pythagorean fuzzy linguistic (IVPFL) information. Moreover, with the increase of attributes and decision makers, the complexity of the operation is a great challenge for making decision. How to overcome multicollinearity is a crucial link in the emergency decision modeling process. In this paper, we treat the attributes and DMs as IVPFL variables and construct the IVPFL principal component analysis (IVPFL-PCA) model to overcome the multicollinearity. Then, a novel TODIM (abbreviation for interactive and multicriterial decision-making in Portuguese) method is proposed to tackle the IVPFL information under several new variables that are independent of each other (i.e., the PCs) and the reasonable weights of PCs obtained based on the IVPFL-PCA model. Finally, a case study on earthquake emergency decision is presented to show the applicability of the proposed approach and some comparisons are presented to illustrate its superiorities.  相似文献   

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