ВЫЧИСЛИТЕЛЬНЫЕ СИСТЕМЫ И СЕТИ
ОБРАБОТКА ИНФОРМАЦИИ И АНАЛИЗ ДАННЫХ
V.G. Sinuk, S.V. Kulabukhov "Method for Classification of Objects with Fuzzy Values of Features"
УПРАВЛЕНИЕ И ПРИНЯТИЕ РЕШЕНИЙ
ПРОГРАММНАЯ ИНЖЕНЕРИЯ
V.G. Sinuk, S.V. Kulabukhov "Method for Classification of Objects with Fuzzy Values of Features"
Abstract. 

The paper is dedicated to the development of classification method for objects, which have their features represented as fuzzy sets. The method is based on computation of the compatibility of the composite premise, which defines an individual class, and the fuzzy features of the objects. The compati-bility is represented by means of fuzzy truth values. The results of defuzzification of these values are used to compare the compatibilities and thus to determine the class of the object. 

Keywords: 

extension principle, fuzzy truth value, linguistic variable. 

PP. 55-59.

DOI 10.14357/20718632220107 
 
Литература

1. Rutkowska, D., M. Pilinsky, and L. Rutkowsky. 2004. Nejronnye seti, geneticheskie algoritmy i nechetkie sistemy [Neural Networks, Genetic Algorithms and Fuzzy Systems]. Moscow: Hot Line – Telecom.
2. Rutkowsky, L. 2010. Metody i tekhnologii iskusstvennogo intellekta [Artificial Intelligence Methods and Technologies]. Moscow: Hot Line – Telecom.
3. Zak, Y.A. 2016. Prinyatie reshenij v usloviyah nechyotkih i razmytyh dannyh: Fuzzy-tekhnologii [Decision Making in the Face of Fuzzy and Blurry Data: Fuzzy Technologies]. Moscow: Lenand.
4. Andrejchikov, A. V., Andrejchikova, O. N. 2021. Nauka i prinyatiya reshenij. Kn. 2: Prinyatie reshenij v usloviyah neopredelennosti. [Science and Decision Making. Book 2: Making Decisions in the Face of Uncertainty]. Moscow: Lenand.
5. Borisov, A.N., A.V. Alexeev, and O.A. Krunberg. 1982. Modeli prinyatiya reshenij na osnove lingvisticheskoj peremennoj [Decision Making Models based on Linguistic Variable]. Riga: Zinatne.
6. Ho, S. Y., Hsieh, C. H., Chen, H. M., Huang, H. L. 2006. Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis. Biosystems. 85:165–176.
7. Sinuk, V.G., and V.V. Mikhelev. 2018. Metody vyvoda dlya sistem logicheskogo tipa na osnove nechetkoj stepeni istinnosti [Inference Methods For Logical Systems based on Fuzzy Degree of Truth]. Izvestiya RAN. Teoriya i sistemy upravleniya [Bulletin of Russian Academy of Sciences. Theory and Control Systems]. 3:108–115.
8. Dubois, D., and A., Prade. 1990. Teoriya vozmozhnostej. Prilozhenie k predstavleniyu znanij v informatike [Possibility Theory. Application to the Representation of Knowledge in Computer Science]. Moscow: Radio and Communication.
9. Sinuk, V.G., and E.V. Pivnenko. 2006. Ob analiticheskom vychislenii nechetkogo znacheniya istinnosti [On the analytical calculation of a fuzzy truth value]. Sbornik trudov Vserossijskoj nauchnoj konferencii po nechetkim sistemam i myagkim vychisleniyam (NSMV-2006) [Proceedings of Russian Scientific Conference on Fuzzy Sys-tems and Soft Computing (FSSC-2006)]. Moscow: Physmathlit. 129–133.
10. Mendel, J.M. 2021. Non-Singleton Fuzzification Made Simpler. Information Sciences. 559:286–308.
 

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