Principal component analysis for interval data

2012 ◽  
Vol 4 (6) ◽  
pp. 535-540 ◽  
Author(s):  
L. Billard ◽  
J. Le-Rademacher
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis

scholarly journals The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

2021 ◽  
Vol 11 (8) ◽  
pp. 3448
Author(s):  
Linchao Yang ◽  
Guozhu Jia ◽  
Fajie Wei ◽  
Wenbing Chang ◽  
Chen Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Data Compression ◽  
Back Propagation ◽  
Prediction Method ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis ◽  
Fault Prediction ◽  
Flight Data

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned aerial vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose and predict UAV faults and improve their safety and reliability. The data-driven fault prediction method provides a basis for UAV fault prediction. A UAV is a typical complex system. Its flight data is a kind of typical high-dimensional large sample dataset, and traditional methods cannot meet the requirements of data compression and dimensionality reduction at the same time. The method used interval data to compress UAV flight data, used CIPCA to reduce the dimensionality of the compressed data, and then used a back propagation (BP) neural network to predict UAV failure. Experimental results show that the CIPCA-BPNN method had obvious advantages over the traditional principal component analysis (PCA)-BPNN method and could accurately predict a failure about 9 s before the UAV failure occurred.

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 on interval data

2006 ◽  
Vol 21 (2) ◽  
pp. 343-363 ◽  
Author(s):  
Federica Gioia ◽  
Carlo N. Lauro
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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