The Lee/Kesler Generalized-Correlation Tables

2019 ◽  
pp. 821-836
Keyword(s):  
Generalized Correlation ◽  
Correlation Tables

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

scholarly journals Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm

2014 ◽  
Vol 21 (2) ◽  
pp. 379-392 ◽  
Author(s):  
R. Calif ◽  
F. G. Schmitt
Keyword(s):  
Wind Speed ◽  
Power Output ◽  
Wind Farm ◽  
Power Spectra ◽  
Physical Space ◽  
Scaling Properties ◽  
Developed Turbulence ◽  
Output Fluctuations ◽  
Wide Range ◽  
Generalized Correlation

Abstract. We consider here wind speed time series and the aggregate output wind power from a wind farm. We study their scaling statistics in the framework of fully developed turbulence and Kolmogorov's theory. We estimate their Fourier power spectra and consider their scaling properties in the physical space. We show that the atmospheric wind speed and the aggregate power output from a wind farm are intermittent and multifractal over a wide range of scales. The coupling between simultaneous data of the wind speed and aggregate power output is investigated through a joint multifractal description using the generalized correlation functions (GCFs). This multiscaling test is compatible with a linear relation between the wind speed and the aggregate power output fluctuations for timescales T ⩾ 103 s ≃ 15 min.

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