fuzzy matrix - ορισμός. Τι είναι το fuzzy matrix
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Τι (ποιος) είναι fuzzy matrix - ορισμός

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.
MATRIX MATH         
  • The vectors represented by a 2-by-2 matrix correspond to the sides of a unit square transformed into a parallelogram.
  • orientation]], since it turns the counterclockwise orientation of the vectors to a clockwise one.
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  • An example of a matrix in Jordan normal form. The grey blocks are called Jordan blocks.
  • An undirected graph with adjacency matrix:
<math display="block">\begin{bmatrix}
1 & 1 & 0 \\
1 & 0 & 1 \\
0 & 1 & 0
\end{bmatrix}.</math>
  • Two different Markov chains. The chart depicts the number of particles (of a total of 1000) in state "2". Both limiting values can be determined from the transition matrices, which are given by <math>
\begin{bmatrix}
 0.7 & 0\\
 0.3 & 1
\end{bmatrix}</math> (red) and <math>
\begin{bmatrix}
 0.7 & 0.2\\
 0.3 & 0.8
\end{bmatrix}</math> (black).
  • Schematic depiction of the matrix product '''AB''' of two matrices '''A''' and '''B'''.
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  • indefinite]].
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RECTANGULAR ARRAY OF NUMBERS, SYMBOLS, OR EXPRESSIONS, ARRANGED IN ROWS AND COLUMNS
Matrix (Mathematics); Matrix (math); Submatrix; Matrix theory; Matrix (maths); Submatrices; Matrix Theory and Linear Algebra; Infinite matrix; Square (matrix); Matrix operation; Square submatrix; Matrix(mathematics); Real matrix; Matrix math; Matrix index; Equal matrix; Matrix equation; Matrix (computer science); Matrix notation; Empty matrix; Real matrices; Principal submatrix; Array (mathematics); Matrix power; Complex matrix; Complex matrices; Applications of matrices; Rectangular matrix; Uniform matrix
<language> An early system on the UNIVAC I or II. [Listed in CACM 2(5):1959-05-16]. (1997-02-27)
Logical matrix         
  • Multiplication of two logical matrices using [[boolean algebra]].
MATRIX WITH ENTRIES FROM THE BOOLEAN DOMAIN B = {0, 1}
Binary matrix; (0,1) matrix; (0,1)-matrix; (0,1)-matrices; 0,1-matrix; 0-1 matrix; Matrix logic; Zero-One matrix; Logical vector; Logical matrices
A logical matrix, binary matrix, relation matrix, Boolean matrix, or (0, 1) matrix is a matrix with entries from the Boolean domain Such a matrix can be used to represent a binary relation between a pair of finite sets.

Βικιπαίδεια

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.