scholarly journals Influencing Factors Study on Green Development Using Principal Component Analysis and Factor Analysis

2021 ◽  
Vol 1952 (4) ◽  
pp. 042120
Author(s):  
Yihong Sun ◽  
Han Gao ◽  
Xuemei Yuan
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Influencing Factors ◽  
Principal Component ◽  
Component Analysis ◽  
Green Development

STUDY ON PLANKTON OF DIFFERENT CATEGORIES OF LAKES IN SUMMER BY MEANS OF PRINCIPAL COMPONENT ANALYSIS, FACTOR ANALYSIS AND CLUSTER ANALYSIS

2010 ◽  
Vol 36 (1) ◽  
pp. 43-50
Author(s):  
Luo-Jun GONG ◽  
Shi-Ping ZHANG ◽  
Bang-Xi XIONG ◽  
Ding-Zhu LIU ◽  
Jin-Zhong LI ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
And Cluster Analysis

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

2022 ◽  
pp. 146808742110707
Author(s):  
Aran Mohammad ◽  
Reza Rezaei ◽  
Christopher Hayduk ◽  
Thaddaeus Delebinski ◽  
Saeid Shahpouri ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Emission Models ◽  
Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

scholarly journals Implementing Horn's Parallel Analysis for Principal Component Analysis and Factor Analysis

2009 ◽  
Vol 9 (2) ◽  
pp. 291-298 ◽  
Author(s):  
Alexis Dinno
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Common Factor ◽  
Principal Component ◽  
Component Analysis ◽  
Parallel Analysis ◽  
Common Factor Analysis

I present paran, an implementation of Horn's parallel analysis criteria for factor or component retention in common factor analysis or principal component analysis in Stata. The command permits classical parallel analysis and more recent extensions to it for the pca and factor commands. paran provides a needed extension to Stata's built-in factor- and component-retention criteria.

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