Дата поступления: 
01.11.2020
Год: 
2020
Номер журнала (Том): 
УДК: 
004.652 : 51-74
DOI: 

10.26731/2658-3704.2020.3(8).22-28

Файл статьи: 
Страницы: 
22
28
Аннотация: 

Existing methods and models for representation of fuzzy numbers in databases of information systems are analyzed. Existing methods for constructing membership functions are reviewed and analyzed. To determine the core of a fuzzy number is proposed to use the data obtained from the investigated object, and for support — theoretical information about the object. The form of the membership function determinate by the ratio of the core to the support of fuzzy set. The criterion values may be revised if necessary, taking into account the values included in the set of real measured data, or based on the requirement to simplify the automated handling procedures. The fuzzy numbers with typical membership are proposed to store in a string that containing values for breaking points. Fuzzy numbers with an arbitrary membership function are proposed to store in a string that containing the coefficients of the approximation function. The developed method can find application in decision support systems and in automated technological process control systems.

Список цитируемой литературы: 

1. Levitskiy S.I., Lysenko Yu. G., Filippov A. V. Modeli, upravleniya proektami v nestabil'noy ekonomicheskoy srede : monografiya [Project management models in an unstable economic environment: monograph]. Donetsk : Yugo-Vostok, 2009. — 354 p.

2. Pegat A. Nechetkoe modelirovanie i upravlenie [Fuzzy modeling and control]. M. : BINOM. Laboratoriya znaniy, 2015. — 801 p.

3. Pospelov D.A. Nechetkie mnozhestva v modelyakh upravleniya i iskusstvennogo intellekta [Fuzzy sets in control and artificial intelligence models]. M.: Nauka. Gl. red. fiz.-mat. lit., 1986. – 312 p.

4. Bashveev Yu.A., Sal'nikov I.I. Funktsiya prinadlezhnosti v sisteme podderzhki prinyatiya resheniya po vyboru mikrokontrollera [The membership functions of decision support system software choosing a microcontroller]. XXI vek: itogi proshlogo i problemy nastoyashchego plyus [XXI century: resumes of the past and challenges of the present plus]. 2016. №3 (31). pp. 89-100

5. Chameau J.L., Santamarina J.C., Membership function I: Comparing methods of Measurement // International Journal of Approximate Reasoning. — 1987. — Vol. 1. — pp.287-301

6. Yager R.R. Nechetkie mnozhestva i teoriya vozmozhnostey. Poslednie dostizheniya [Fuzzy set and possibility theory. Recent Development]. M.: Radio i svyaz', 1986. – 408 p.

7. Orlov A.I. Ekspertnye otsenki [Theory of expert estimates in our country]. Zhurnal «Zavodskaya laboratoriya» [Industrial Laboratory]. 1996. — T.62. № 1. — pp.54-60.

8. Globa L.S. , Ternovoj M.Yu., Shtogrina E.S. Podhod k hraneniyu baz nechetkih znanij [An approach to storing fuzzy knowledge bases]. OSTIS-2012. – pp. 99-102.

9. Jose Galindo. Fuzzy databases: modeling, design and implementation / Jose Galindo, Angelica Urrutia, Mario Piattini // IGP in USA, – 2006.

10. Azov M.S., Yarushkinoj N. G. Prikladnye  intellektualnye  sistemy,  osnovannye  na  myagkih  vychisleniyah [Applied Intelligent Systems Based on Soft Computing]. Ulyanovsk: UlGTU, 2004 – 139 p.

11. Kasatkina S.V., Tanyanskij S.S., Filatov V.A. Metody hraneniya i obrabotki nechetkih dannyh v srede relyacion-nyh sistem [Methods for storing and processing fuzzy data in a relational system environment]. AAEKS. – «Informacionno- upravlyayushie kompleksy i sistemy» [Information and control complexes and systems]. – 2009. –  № 2(24).

12. Sergienko M.A. Metody proektirovaniya nechetkoj bazy znanij [Fuzzy Knowledge Base Design Techniques]. Vestnik VGU, Seriya: Sistemnyj analiz i informacionnye tehnologii [System Analysis and Information Technologies]. – 2008. – № 2. – pp. 67-71.

13. Naresh Kumar. Storing, Querying and Validating Fuzzy XML Data in Relational Database / Naresh Kumar, Satyanand Reddy, V.E.S. Murthy // (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, pp. 5233-5240.

14. Jian Liu. Formal transformation from fuzzy object-oriented databases to fuzzy XML / Jian Liu, Z. M. Ma // Applied Intelligence Volume 39, Issue 3, 2013, pp 630–641

15. Amit Garg. Querying Capability Enhancement in Database Using Fuzzy Logic / Amit Garg, Dr. Rahul Rishi // Global Journal of Computer Science and Technology Volume 12 Issue 6 Version 1.0 March 2012.

16. Bizyanov E.E. Implementaciya nechetkih modelej v informacionnye sistemy ekono-micheskih obektov [Implementation of fuzzy models in information systems of economic objects]. Ekonomika i menedzhment innovacionnyh tehnologij [Economics and management of innovative technologies]. 2015. – № 4. – [Elektronnyj resurs]. – Rezhim dostupa: http://ekonomika.snauka.ru/2015/04/8351

17. Berestov V.L. Zhilenkova E.P., Kuznetsov S.G. Statistika: Uchebnoe posobie [Statistics: Study guide]. Bryansk: Bryan.gos.inzh.-tekhn.akad., 2014. – 244 p.