fuzzy coefficient - определение. Что такое fuzzy coefficient
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Что (кто) такое fuzzy coefficient - определение

SYSTEM FOR REASONING ABOUT VAGUENESS
Fuzzy Logic; Fuzzy logician; Formal fuzzy logic; Fuzzy transportation; Fuzzy Transportation; Fuzzy logics; Fuzzy databases; Applications of fuzzy logic; Compensatory fuzzy logic; Fuzzy inference; Fuzzy inference system; Linguistic variable (fuzzy logic); Zadeh operator; Fuzzy Turing machine; Fuzzy database; Fuzzy relational database; Propositional fuzzy logic; Gödel fuzzy logic; Product fuzzy logic; Probability and fuzzy logic; Fuzzy logic and probability

fuzzy subset         
  • Some Key Developments in the Introduction of Fuzzy Set Concepts.<ref name="CADsurvey"/>
SETS WHOSE ELEMENTS HAVE DEGREES OF MEMBERSHIP
Fuzzy sets; Fuzzy set theory; Fuzzification; Fuzzy subset; Credibility(fuzzy); Fuzzy category; Goguen category; Fuzzy Sets; Fuzzy relation equation; Pythagorean fuzzy set; Degree of membership; Uncertain set
In fuzzy logic, a fuzzy subset F of a set S is defined by a "membership function" which gives the degree of membership of each element of S belonging to F.
Attenuation coefficient         
MEASURE FOR THE EXPONENTIAL REDUCTION OF A QUANTITY ALONG A PATH DUE TO ABSORPTION AND SCATTERING
Absorption coefficient; Absorption Coefficient; Linear attenuation coefficient; Linear coefficient; Linear absorption coefficient; Narrow beam attenuation coefficient; Scattering coefficient
The linear attenuation coefficient, attenuation coefficient, or narrow-beam attenuation coefficient characterizes how easily a volume of material can be penetrated by a beam of light, sound, particles, or other energy or matter. A coefficient value that is large represents a beam becoming 'attenuated' as it passes through a given medium, while a small value represents that the medium had little effect on loss.
Pearson correlation coefficient         
  • Several sets of (''x'',&nbsp;''y'') points, with the correlation coefficient of ''x'' and ''y'' for each set. The correlation reflects the strength and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case the correlation coefficient is undefined because the variance of ''Y'' is zero.
  • Critical values of Pearson's correlation coefficient that must be exceeded to be considered significantly nonzero at the 0.05 level.
  • prediction interval]] for ''Y'' may be reduced given the corresponding value of ''X''. For example, if ''ρ'' = 0.5, then the 95% prediction interval of ''Y''&#124;''X'' will be about 13% smaller than the 95% prediction interval of ''Y''.
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TYPE OF COEFFICIENT
Pearson r; Pearsonr; Pearson's correlation coefficient; Pearson's r; Pearson's correlation; Product-moment correlation coefficient; Pearson product moment correlation coefficient; Product moment correlation coefficient; Pearson coefficient; Bivariate correlation; Pearson’s correlation coefficient; Pearson's product; Pearson product-moment correlation; Pearson product-moment; Product-moment correlation; Pearson Product Moment Correlation Coefficient; Pearson's coefficient of correlation; Pearson product–moment correlation coefficient; PPMCC; Pearson product-moment correlation coefficient; Pearson correlation; Pearson’s Correlation Coefficient; Pearson's linear correlation coefficient; Circular correlation coefficient; Pearson's product-moment correlation
In statistics, the Pearson correlation coefficient (PCC, pronounced ) ― also known as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), the bivariate correlation, or colloquially simply as the correlation coefficient ― is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1.

Википедия

Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh. Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty.

Fuzzy logic has been applied to many fields, from control theory to artificial intelligence.