scholarly journals Social Effect Analysis of Intelligent Sports Based on Principal Component Analysis and Fuzzy Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaobo Chen
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Control ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis ◽  
Data Matrix ◽  
Fuzzy Data ◽  
Social Effect ◽  
Effect Analysis ◽  
The Masses

In order to explore the social effects of intelligent sports systems, this paper combines principal component analysis technology and fuzzy control technology to construct an intelligent sports social effect analysis system. The original fuzzy data is expressed linearly by structural elements, and the original fuzzy data matrix is divided into a main data matrix part and an error data matrix part. According to the principal component analysis method of fuzzy data represented by structural elements, this paper studies the principal component analysis method of interval data using the left end point matrix, right end point matrix, and midpoint matrix of interval data. In addition, this article uses principal component analysis and fuzzy control to study the response of the intelligent motion system to the masses and conducts experiments to analyze the social effects. It can be seen from experimental research that the intelligent sports system constructed in this article has a high degree of satisfaction of the masses, which proves that the intelligent sports have a certain social effect.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals SVD-based principal component analysis of geochemical data

2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Geochemical Data ◽  
Average Group ◽  
Testing Data ◽  
Value Decomposition ◽  
Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

scholarly journals New Approach to Generalized Two-Dimensional Correlation Spectroscopy. 1: Combination of Principal Component Analysis and Two-Dimensional Correlation Spectroscopy

2002 ◽  
Vol 56 (12) ◽  
pp. 1562-1567 ◽  
Author(s):  
Young Mee Jung ◽  
Hyeon Suk Shin ◽  
Seung Bin Kim ◽  
Isao Noda
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Correlation Spectroscopy ◽  
Data Matrix ◽  
Two Dimensional ◽  
Noise Component ◽  
Reconstructed Data ◽  
2D Correlation Analysis

The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the significant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.

scholarly journals Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis

2018 ◽  
Vol 11 (3) ◽  
pp. 1162 ◽  
Author(s):  
S.M. Shaharudin ◽  
N. Ahmad ◽  
N.H. Zainuddin ◽  
N.S. Mohamed
Keyword(s):  
Principal Component Analysis ◽  
Simulated Data ◽  
Principal Component ◽  
Breakdown Point ◽  
Component Analysis ◽  
Data Matrix ◽  
Robust Pca ◽  
Data Set ◽  
Number Of Components ◽  
Rainfall Patterns

A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and  compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components .The results showed a  more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality.

scholarly journals Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

Solid Earth ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 515-524 ◽  
Author(s):  
L. W. Xie ◽  
J. Zhong ◽  
F. F. Chen ◽  
F. X. Cao ◽  
J. J. Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Microbial Biomass ◽  
Soil Fertility ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Microbial Biomass Nitrogen ◽  
Changing Trend

Abstract. Expanding of karst rocky desertification (RD) area in southwestern China is strangling the sustainable development of local agricultural economy. It is important to evaluate the soil fertility at RD regions for the sustainable management of karst lands. The changes in 19 different soil fertility-related variables along a gradient of karst rocky desertification were investigated in five different counties belonging to the central Hunan province in China. We used principal component analysis method to calculate the soil data matrix and obtained a standardized integrate soil fertility (ISF) indicator to reflect RD grades. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorus, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) was potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of ISF was basically parallel to the aggravation of RD, and the strength of ISF mean values were in the order of PRD > LRD > MRD > IRD. The TOC, MBC, and MBN could be regarded as the key indicators to evaluate the soil fertility.

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