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.
| Mualliflar | Мухамедиева, Д.Т. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2022-12-21 |
| Jild | 2 |
| Son | 2 |
| Betlar | 85-93 |
| Til | Rus |
fuzzy set, production rules, fuzzy inference, fuzzy model, knowledge base, expert knowledge matrix, algorithm, alternative, decision making, нечеткое множество, правила продукций, нечеткий вывод, нечеткая модель, база знаний, экспертная матрица знаний, алгоритм, альтернатива принятий решений
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Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar