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The Pre-test Principal Components Estimator in the Two Seemingly Unrelated Regression System
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作者 归庆明 《Chinese Quarterly Journal of Mathematics》 CSCD 1996年第4期57-61, ,共5页
For the two seemingly unrelated regression system, this paper proposed a new type of estimator called pre-test principal components estimator (PTPCE) and discussed some properties of PTPCE.
关键词 semmingly unrelated regression system pre-test principal components estimator
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Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length
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作者 SHI Yu-feng~(1, 2) (1. Shandong University of Technology, Zibo 255049, China 2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China) 《中国有色金属学会会刊:英文版》 CSCD 2005年第S1期153-155,共3页
Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description... Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem. 展开更多
关键词 minimum DESCRIPTION LENGTH Gauss-Markov MODEL multi-collinearity principal componENT estimATION
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Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation 被引量:16
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作者 熊丽 梁军 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2007年第4期524-532,共9页
Abstract Data-driven tools, such as principal component analysis (PCA) and independent component analysis (ICA) have been applied to different benchmarks as process monitoring methods. The difference between the t... Abstract Data-driven tools, such as principal component analysis (PCA) and independent component analysis (ICA) have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation (KDE) is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE(KPCA), and ICA with KDE,(KICA), are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator. 展开更多
关键词 multivariate statistical process monitoring principal component analysis kermel density estimation POLYPROPYLENE catalyzer reactor fault detection data-driven tools
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中国创新驱动水平测度、地区差异与动态演进 被引量:1
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作者 唐娟 王华 秦放鸣 《统计与决策》 CSSCI 北大核心 2024年第4期56-61,共6页
创新驱动是高质量发展的第一动力,文章界定了创新驱动的内涵,明确其内在作用机制,在此基础上概括出认知基础、主体投入、成果产出和环境支撑四大维度。基于四大维度构建创新驱动水平评价指标体系,运用主成分分析法测算出创新驱动水平,... 创新驱动是高质量发展的第一动力,文章界定了创新驱动的内涵,明确其内在作用机制,在此基础上概括出认知基础、主体投入、成果产出和环境支撑四大维度。基于四大维度构建创新驱动水平评价指标体系,运用主成分分析法测算出创新驱动水平,并借助Dagum基尼系数和Kernel密度估计方法揭示创新驱动水平的区域差异及演进趋势。结果表明:(1)虽然中国整体创新驱动水平大幅度提高,但是,地区之间的差异也越来越大,东部地区的创新驱动发展水平显著高于中西部地区;(2)各省份间的创新驱动水平差距较大,甚至部分省份的创新驱动水平在某些年份出现倒退的情况;(3)创新驱动的制度保障还不完善,地区之间的技术扩散和转移机制不畅,造成了不同的创新驱动发展路径。 展开更多
关键词 创新驱动 地区差异 主成分分析 Kernel密度估计
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A New Class of Biased Linear Estimators in Deficient-rank Linear Models 被引量:1
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作者 归庆明 段清堂 +1 位作者 周巧云 郭建锋 《Chinese Quarterly Journal of Mathematics》 CSCD 2001年第1期71-78,共8页
In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias es... In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment. 展开更多
关键词 deficient_rank model best linear minimum bias estimator generalized principal components estimator mean squared error condition number
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基于M估计算法的三维点云平面拟合方法研究
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作者 杨少舟 龙东平 +2 位作者 陈继尧 吴士旭 徐先懂 《电子测量技术》 北大核心 2024年第5期70-76,共7页
通过激光传感器获取的三维点云难免混入噪声和异常点,导致点云平面的拟合精度降低。为解决该问题,本文提出了一种结合M估计样本一致性(MSAC)算法和主成分分析(PCA)法拟合点云平面的方法。该方法首先通过MSAC算法去除点云数据中的异常点... 通过激光传感器获取的三维点云难免混入噪声和异常点,导致点云平面的拟合精度降低。为解决该问题,本文提出了一种结合M估计样本一致性(MSAC)算法和主成分分析(PCA)法拟合点云平面的方法。该方法首先通过MSAC算法去除点云数据中的异常点,获得较为理想的点云平面,然后使用PCA方法对保留的点云数据进行平面拟合,以获取更加精确的点云平面参数。使用电池托盘作为被测物,应用3D线激光轮廓传感器扫描被测物并将点云数据传输到计算机进行处理。通过设定的仿真数据和电池托盘点云数据进行实验,发现本文方法与随机采样一致性(RANSAC)结合PCA、最小平方中值(LMedS)结合PCA的方法相比,在耗时接近的情况下,能够显著降低异常点对点云平面拟合的影响,获得更精确的平面拟合参数。对两个部分的电池托盘点云滤波处理后进行平面拟合时,能够发现本文方法与其他两种方法相比,标准差分别降低了28.6%和22.5%%、24.0%和29.0%,该方法具有较高的平面拟合精度和实用性。 展开更多
关键词 点云数据 异常点 平面拟合 M估计 主成分分析方法
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基于DPC-MND和多元状态估计的磨煤机故障预警研究
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作者 姚天杨 茅大钧 韩万里 《控制工程》 CSCD 北大核心 2024年第5期928-937,共10页
火电机组磨煤机存在运行条件恶劣、故障频发等问题,对磨煤机进行故障预警,可以有效防止一些常见故障的发生,从而保证火电机组的安全运行。为此,提出一种基于相互邻近度的密度峰值聚类和多元状态估计的磨煤机故障预警方法。首先,采用核... 火电机组磨煤机存在运行条件恶劣、故障频发等问题,对磨煤机进行故障预警,可以有效防止一些常见故障的发生,从而保证火电机组的安全运行。为此,提出一种基于相互邻近度的密度峰值聚类和多元状态估计的磨煤机故障预警方法。首先,采用核主元分析选取磨煤机的主要状态参数,同时采用集合经验模态分解对历史运行数据进行去噪,进一步优化数据质量;然后,采用基于相互邻近度的密度峰值聚类(density peaks clustering based on mutual neighborhood degrees,DPC-MND)方法构建动态记忆矩阵,利用多元状态估计技术(multivariate state estimation techniques,MSET)对磨煤机正常运行工况下的历史数据进行建模,并确定磨煤机的运行状态。最后,以安徽某电厂ZGM113G型中速磨煤机为例进行验证,结果表明该方法可以实现对磨煤机故障的有效预警。 展开更多
关键词 中速磨煤机 核主元分析 DPC-MND 多元状态估计技术 故障预警
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Application of K-means and PCA approaches to estimation of gold grade in Khooni district(central Iran) 被引量:3
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作者 Neda Mahvash Mohammadi Ardeshir Hezarkhani Abbas Maghsoudi 《Acta Geochimica》 EI CAS CSCD 2018年第1期102-112,共11页
Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have ... Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation. 展开更多
关键词 K-means method CLUSTERING principal component analysis(PCA) estimATION GOLD Khooni district
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Head pose estimation method based on pose manifold and tensor decomposition
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作者 Wei Wei Yanning Zhang Chunna Tian 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第5期907-913,共7页
Pose manifold and tensor decomposition are used to represent the nonlinear changes of multi-view faces for pose estimation,which cannot be well handled by principal component analysis or multilinear analysis methods.A... Pose manifold and tensor decomposition are used to represent the nonlinear changes of multi-view faces for pose estimation,which cannot be well handled by principal component analysis or multilinear analysis methods.A pose manifold generation method is introduced to describe the nonlinearity in pose subspace.And a nonlinear kernel based method is used to build a smooth mapping from the low dimensional pose subspace to the high dimensional face image space.Then the tensor decomposition is applied to the nonlinear mapping coefficients to build an accurate multi-pose face model for pose estimation.More importantly,this paper gives a proper distance measurement on the pose manifold space for the nonlinear mapping and pose estimation.Experiments on the identity unseen face images show that the proposed method increases pose estimation rates by 13.8% and 10.9% against principal component analysis and multilinear analysis based methods respectively.Thus,the proposed method can be used to estimate a wide range of head poses. 展开更多
关键词 head pose estimation principal component analysis multilinear algebra manifold analysis.
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Outlier Mining Based on Principal Component Estimation
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作者 HuYang TingYang 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2005年第2期303-310,共8页
Outlier mining is an important aspect in data mining and the outlier miningbased on Cook distance is most commonly used. But we know that when the data have multicollinearity,the traditional Cook method is no longer e... Outlier mining is an important aspect in data mining and the outlier miningbased on Cook distance is most commonly used. But we know that when the data have multicollinearity,the traditional Cook method is no longer effective. Considering the excellence of the principalcomponent estimation, we use it to substitute the least squares estimation, and then give the Cookdistance measurement based on principal component estimation, which can be used in outlier mining.At the same time, we have done some research on related theories and application problems. 展开更多
关键词 Outlier mining principal component estimation Cook distance
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A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images
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作者 Abdallah Bengueddoudj Zoubeida Messali Volodymyr Mosorov 《Journal of Innovative Optical Health Sciences》 SCIE EI CAS 2017年第3期52-68,共17页
In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Poste... In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method. 展开更多
关键词 Medical imaging multimodal medical image fusion scale mixing complex wavelet transform MAP Bayes estimation principal component analysis.
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ESTIMATION OF ROCK-AGGREGATE VOLUME BASED ON PCA AND LM-OPTIMIZED NEURAL NETWORK
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作者 Zhao Pan Chen Ken Wang Yicong Zhang Yun 《Journal of Electronics(China)》 2009年第6期825-830,共6页
In granule processing industries, acquisition of particle size and shape parameters is a common procedure, and volumetric measurement is of great importance in dealing with particle sizing and gradation. To eradicate ... In granule processing industries, acquisition of particle size and shape parameters is a common procedure, and volumetric measurement is of great importance in dealing with particle sizing and gradation. To eradicate the major drawbacks with manual gauge, this paper proposes an optical approach using Back Propagation (BP) neural network to estimate the particle volume based on the two-Dimensional (2D) image information. To achieve the better network efficiency and structure simplicity, Principal Component Analysis (PCA) is adopted to reduce the dimensions of network inputs To overcome the shortcomings of generic BP network for being slow to converge and vulnerable to being trapped in local minimum, Levenberg-Marquardt (LM) algorithm is applied to achieve a higher speed and a lower error rate. The real particle data is utilized in training and testing the presented network. The experimental result suggests that the proposed neural network is capable of estimating aggregate volume with satisfactory precision and superior to the generic BP network in terms of perforxnance capacity. 展开更多
关键词 Particle image Particle parameters principal component Analysis (PCA) NEURALNETWORK Volume estimation
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AGGREGATE VOLUMETRIC ESTIMATION BASED ON PCA AND MOMENTUM-ENHANCED BP NEURAL NETWORK
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作者 Chen Ken Zhao Pan +1 位作者 Batur Celal Zhang Yun 《Journal of Electronics(China)》 2009年第5期637-643,共7页
This paper proposes a Back Propagation (BP) neural network with momentum enhancement aiming to achieving the smooth convergence for aggregate volumetric estimation purpose. Network inputs are first selected by optical... This paper proposes a Back Propagation (BP) neural network with momentum enhancement aiming to achieving the smooth convergence for aggregate volumetric estimation purpose. Network inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image. To simplify the network structure, principal component analysis technique is applied to reduce the input dimension. The specific network structure is finalized based on both empirical expertise and analysis on selecting the appropriate number of neurons in hidden layer. The network is trained using the finite number of randomly-picked particles. The training and test results suggest that, compared to the generic BP network, the training duration of the proposed neural network is greatly attenuated, the complexity of the network structure is largely reduced, and the estimation precision is within 2%, being sufficiently up to technical satisfaction. 展开更多
关键词 Aggregate volume Back Propagation (BP) neural network MOMENTUM Volume estimate principal component Analysis (PCA)
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基于单目视觉的手术器械位姿估计模型研究 被引量:5
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作者 王巍 白天宇 《激光杂志》 CAS 北大核心 2023年第1期32-41,共10页
手术器械管理是整个手术过程中重要的一环,科学高效的管理手术器械对提高医疗质量具有重要意义。为有效解决人工传递和清点手术器械效率低、易出错的情况,提出了一种基于单目视觉的手术器械位姿估计模型。首先将手术器械数据集图像进行G... 手术器械管理是整个手术过程中重要的一环,科学高效的管理手术器械对提高医疗质量具有重要意义。为有效解决人工传递和清点手术器械效率低、易出错的情况,提出了一种基于单目视觉的手术器械位姿估计模型。首先将手术器械数据集图像进行Gamma适应校正,其次把校正后的数据集放入YOLOV5进行模型权重训练与识别验证,接着将识别所得的单枚手术器械图像进行局部增强与主体提取,最后把处理结果接入到位姿估计模块中,得到手术器械的中心点与4D位姿。实验结果表明,手术器械识别精度可达到89.4%,平均平移位姿误差为3.482 mm,平均旋转角度误差为2.048度,并且在不同分辨率和灰度值下均有良好的表现,验证了该算法具有良好的精度和鲁棒性。 展开更多
关键词 手术器械 单目视觉 位姿估计 YOLOV5 PCA主成分分析
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基于KPCA的膝关节多模式连续运动估计
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作者 张建华 王豪 +1 位作者 李克祥 王唱 《中国医学物理学杂志》 CSCD 2023年第6期742-749,共8页
为了实现不同运动模式下膝关节连续运动的有效估计,提出一种基于核主成分分析(KPCA)的下肢膝关节连续运动估计方法。首先,融合多维表面肌电信号时域特征获取不同运动模式下较为全面的运动信息;其次,采用KPCA方法进行肌电特征降维,获取... 为了实现不同运动模式下膝关节连续运动的有效估计,提出一种基于核主成分分析(KPCA)的下肢膝关节连续运动估计方法。首先,融合多维表面肌电信号时域特征获取不同运动模式下较为全面的运动信息;其次,采用KPCA方法进行肌电特征降维,获取与该类运动模式最为相关的主成分向量,并基于反向传播神经网络实现不同运动模式下膝关节连续运动的有效估计;最后,对5个实验对象的4种运动模式进行实验验证。结果表明该方法不仅可有效估计不同运动模式下膝关节连续运动角度,相对于PCA算法估计精度也有明显提高。 展开更多
关键词 表面肌电信号 核主成分分析 时域特征 连续运动估计
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基于群体优化-概率神经网络的配电网设备状态研判模型 被引量:3
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作者 解明辉 孙亚剑 +1 位作者 汤思杰 曹晖 《电工电能新技术》 CSCD 北大核心 2023年第6期79-87,共9页
随着我国电能需求量不断提升,配电网可靠性要求逐步提高,配电网设备状态研判难度也不断增大。针对该问题,本文提出一种基于群体优化-概率神经网络的配电网设备状态研判模型。引入改进后的人工鱼群算法对概率神经网络的平滑因子进行寻优... 随着我国电能需求量不断提升,配电网可靠性要求逐步提高,配电网设备状态研判难度也不断增大。针对该问题,本文提出一种基于群体优化-概率神经网络的配电网设备状态研判模型。引入改进后的人工鱼群算法对概率神经网络的平滑因子进行寻优,避免其因随机设置而导致研判精度不理想的问题。基于群体优化-概率神经网络算法建立设备状态研判模型,同时利用合成少数类过采样技术改善配电网数据集不平衡的问题,采用主成分分析法对数据集进行特征属性指标提取,减少冗余指标对状态研判精度和时间的影响。实验结果表明,本文模型在状态研判的精度和计算时间上均具有一定优势,能够在配电网的状态研判过程中起到辅助作用。 展开更多
关键词 状态研判模型 概率神经网络 人工鱼群算法 合成少数类过采样 主成分分析
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基于主成分分析的深度前馈神经网络的肾小球滤过率估算算法
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作者 王露露 杨震 +3 位作者 黄山 张罡 李飞 詹曙 《北京生物医学工程》 2023年第2期164-169,共6页
目的提出一种基于主成分分析(principal component analysis,PCA)的深度前馈神经网络(deep feedforward neural network,DFNN),建立一个适用于中国慢性肾脏病(chronic kidney disease,CKD)人群的肾小球滤过率(glomerular filtration rat... 目的提出一种基于主成分分析(principal component analysis,PCA)的深度前馈神经网络(deep feedforward neural network,DFNN),建立一个适用于中国慢性肾脏病(chronic kidney disease,CKD)人群的肾小球滤过率(glomerular filtration rate,GFR)估算模型,并探讨其在慢性肾脏病患者肾小球滤过率估算中的应用。方法受试者为2019年5月—2021年1月就诊于安徽医科大学第二附属医院,排除年龄<18岁的肾功能不稳定,服用甲氧苄啶或西咪替丁或接受透析后的163例患者。本研究以99m Tc-DTPA肾动态显像测定GFR为标准,建立主成分分析的深度前馈神经网络(deep feedforward neural network,DFNN)模型,以此估算GFR,同时将估算GFR结果与传统CG方程和BP神经网络估算结果进行对比分析。结果通过PCA-DFNN-1神经网络训练出来的估算模型的15%符合率、30%符合率、50%符合率分别为38.77%、55.1%、75.5%;ROC曲线下面积为0.845;Youden指数为0.58。结论提出的基于主成分分析的深度前馈神经网络模型有优于CG方程和BP神经网络模型的结果,可以用于估算GFR。 展开更多
关键词 慢性肾脏病 肾小球滤过率 主成分分析 深度前馈神经网络 估算模型
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利用卷积神经网络的土壤有机质含量高光谱估测
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作者 刘杰亚 李西灿 +1 位作者 任文静 吴亚楠 《河北农业大学学报》 CAS CSCD 北大核心 2023年第4期118-124,共7页
为提高土壤有机质高光谱估测精度,以山东省济南市章丘区的76个土壤样本有机质含量及其高光谱数据为基础,建立基于卷积神经网络的土壤有机质含量估测模型。首先对原始高光谱数据进行预处理,利用主成分分析对光谱数据降维,并转化为四维光... 为提高土壤有机质高光谱估测精度,以山东省济南市章丘区的76个土壤样本有机质含量及其高光谱数据为基础,建立基于卷积神经网络的土壤有机质含量估测模型。首先对原始高光谱数据进行预处理,利用主成分分析对光谱数据降维,并转化为四维光谱信息数组,通过实验模拟调整各项参数及网络结构得到最优估测模型。结果表明:当模型采用1个3×3的卷积核,1个平均池化层,1个完全连接层,且网络计算迭代600次时,卷积神经网络模型达到最优预测效果,其中12个检验样本估测结果的决定系数为R^(2)=0.841,平均相对误差为7.123%,精度均优于传统模型。研究表明利用卷积神经网络估测土壤有机质含量是可行有效的。 展开更多
关键词 卷积神经网络 土壤有机质 高光谱遥感 主成分分析 光谱估测
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基于改进支持向量回归的船用机械零部件寿命估计 被引量:1
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作者 张丽娜 凌付平 《舰船科学技术》 北大核心 2023年第6期186-189,共4页
船用机械零部件退化的敏感特征难以提取,导致其寿命估计均方误差增加。为此,设计一种基于改进支持向量回归的船用机械零部件寿命估计方法。采用小波变换法去除全寿命周期数据噪声,提取零部件退化的时域特征,利用集合经验模态分解获取频... 船用机械零部件退化的敏感特征难以提取,导致其寿命估计均方误差增加。为此,设计一种基于改进支持向量回归的船用机械零部件寿命估计方法。采用小波变换法去除全寿命周期数据噪声,提取零部件退化的时域特征,利用集合经验模态分解获取频域特征。经主成分分析法完成特征降维处理后,确定机械零部件退化的敏感特征。采用考虑莱维飞行机制的改进蚁狮优化算法寻求支持向量回归模型最佳参数。将提取到的敏感特征输入至改进支持向量回归模型中,得到船用机械零部件寿命估计值。实验结果表明,当步长为6时,支持向量回归模型的均方误差指标最小、决定系数指标最大,可实现机械零部件寿命精准估计。 展开更多
关键词 支持向量回归 机械零部件 寿命估计 退化状态 主成分分析
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线性回归模型中的KL型主成分估计 被引量:1
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作者 黄丹 黄介武 《湖南文理学院学报(自然科学版)》 CAS 2023年第2期7-14,共8页
当线性回归模型中存在复共线性时,基于最小二乘估计的统计推断往往会受到影响。鉴于此,结合主成分估计和KL估计,提出了一类新的估计方法,即KL型主成分估计,以期克服复共线性问题。同时,得到新的估计在均方误差意义下优于最小二乘估计、... 当线性回归模型中存在复共线性时,基于最小二乘估计的统计推断往往会受到影响。鉴于此,结合主成分估计和KL估计,提出了一类新的估计方法,即KL型主成分估计,以期克服复共线性问题。同时,得到新的估计在均方误差意义下优于最小二乘估计、主成分估计、r-k估计、r-d估计和KL估计的充要条件。并利用Monte Carlo模拟和实证分析对各估计量在均方误差准则下进行了比较。 展开更多
关键词 线性回归模型 复共线性 主成分估计 KL估计 均方误差
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