principal component analysis

基本解釋

The principal component analysis(PCA) approach for sensor fault detection, identification and reconstruction in HVAC system is presented.
提出了空調(diào)系統(tǒng)傳感器故障檢測、故障識別、故障重構(gòu)的主成分分析方法。
Through principal component analysis, we have synthesized numerous indexes, (eliminated) information overlapping of the sample, and reduced the input dimension of BP network.
通過主成分分析法將眾多指標進行綜合，消除樣本間的信息重疊，降低BP網(wǎng)絡(luò)的輸入維數(shù)。
Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures.
首先對歷史光譜數(shù)據(jù)進行主成分分析（PCA）處理，再根據(jù)PCA特征數(shù)據(jù)與其表面溫度的對應(yīng)關(guān)系建立溫度的估計模型，該模型是基于高斯核函數(shù)的。

主成分分析
By principal component analysis, we get four main components.
通過主成分分析,得到了4個主成分。
主成份分析
The paper puts the indexes of effective competition in order on the basis of the SCP theory, and constructs a principal component analysis model.
本文以SCP理論為基礎(chǔ),歸納了有效競爭評價指標體系,建立了有效競爭主成份分析模型。
計算機科學(xué)技術(shù)
主成分分析
Commonly, Principal Component Analysis (PCA) is used to remove the correlation.
一般用主成分分析去除波段之間的相關(guān)性。
主元分析
The content includes in the fuzzy segment,BP neural networks and principal component analysis.
內(nèi)容涉及模糊分割技術(shù)、BP神經(jīng)網(wǎng)絡(luò)、主元分析技術(shù)。
主分量分析
Some kinds of complex methods were discussed in this paper, which combined wavelet, Principal Component Analysis (PCA) and kernel function teconology.
論文嘗試將小波技術(shù)、主分量分析及核方法相結(jié)合,用于處理這類信號。
主要成分分析

主成分回歸
Methods used in demonstration-simulation include statistic description and statistic test methods, such as average, non-parametric statistics of two independent samples; induction; methods of multivariate statisticalanalysis, such as multiple linear regression, principal component analysis, etc. ; literature review and so on.3.
實證模擬中應(yīng)用的具體方法包括各種統(tǒng)計描述和檢驗方法，如平均數(shù)、二獨立樣本的非參數(shù)檢驗；歸納的方法；多因素分析法，包括多元線性回歸、主成分回歸等；文獻復(fù)習法等。
電子、通信與自動控制技術(shù)
主成份分析