Рақамли технологияларнинг назарий ва амалий масалалари 2-jild 2-son (2022) · 85-93-betlar

Learning Algorithm for Matrix Representation of Fuzzy Logical Inclusion Systems

Мухамедиева, Д.Т.

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Annotatsiya

The application of fuzzy logic theory in solving classification problems makes it possible to obtain fundamentally new models and methods for analyzing these systems. A neuro-fuzzy algorithm for the synthesis of fuzzy inference systems is proposed. A two-stage adaptive algorithm for the synthesis of fuzzy inference systems is described. At the first stage, the initial fuzzy parameters are clustered in order to reduce the number of input parameters of fuzzy rules, and at the second stage, fuzzy models (inference rules) of the Mamdani type are synthesized and the matrix representation of fuzzy logic is used to solve classification problems.

fuzzy setproduction rulesfuzzy inferencefuzzy modelknowledge baseexpert knowledge matrixalgorithmalternativedecision makingнечеткое множество

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Iqtibos olish

APA 7 
Мухамедиева, Д.Т. (2022). Learning Algorithm for Matrix Representation of Fuzzy Logical Inclusion Systems. Рақамли технологияларнинг назарий ва амалий масалалари, 2(2), 85-93.
GOST R 7.0.5 
Мухамедиева, Д.Т. Learning Algorithm for Matrix Representation of Fuzzy Logical Inclusion Systems // Рақамли технологияларнинг назарий ва амалий масалалари. 2022. Т. 2. № 2. С. 85-93.
BibTeX 
@article{д.т.2022,
  author  = {Мухамедиева, Д.Т.},
  title   = {Learning Algorithm for Matrix Representation of Fuzzy Logical Inclusion Systems},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year    = {2022},
  volume  = {2},
  number  = {2},
  pages   = {85-93}
}
RIS 
TY  - JOUR
AU  - Мухамедиева, Д.Т.
TI  - Learning Algorithm for Matrix Representation of Fuzzy Logical Inclusion Systems
JO  - Рақамли технологияларнинг назарий ва амалий масалалари
PY  - 2022
VL  - 2
IS  - 2
SP  - 85
EP  - 93
ER  -