DATA PROCESSING AND ANALYSIS
MATHEMATICAL MODELING
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
T. A. Moiseeva, Т. М. Ledeneva Knowledge Base Generation Based on Fuzzy Clustering
MANAGEMENT AND DECISION MAKING
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
T. A. Moiseeva, Т. М. Ledeneva Knowledge Base Generation Based on Fuzzy Clustering
Abstract. 

The article states fuzzy Takagi-Sugeno rule base generation problem based on ellipsoidal clustering. After obtaining clusters of ellipsoidal shape the problem of building minimal volume ellipsoids, enclosing all clusters points, appears. The premises of the generated fuzzy rules are formed by constructing projections of ellipsoids on the coordinate axis, and conclusions – either using ellipsoid axes, or based on the projection. In the article, the authors suggest to use Khachiyan’s algorithm for building minimal volume enclosing ellipsoid in order to increase the accuracy of approximation and they compare two approaches of choosing optimal parameters of ellipsoids which enclose all clusters points.

Keywords: clustering algorithms, “if-then” rules, knowledge base, fuzzy systems.

PP. 97-108.

DOI 10.14357/20718632230110
 
References

1. Sunardi, A. Yudhana, Furizal. 2023. Tsukamoto Fuzzy Inference System on Internet of Things-Based for Room Temperature and Humidity Control. IEEE Access. 11:6209-6227.
2. Lin, J., X. Liu, Z. Ren. 2022. Fuzzy PID Control for Multi-joint Robotic Arm. 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), Perth, Australia. 723-728.
3. Sanaeva, G. N., A. E. Prorokov, V. N. Bogatikov, D. P.Vent. 2020. Ierarhicheskaya sistema nechetkogo regulirovaniya processa polucheniya acetilena okislitelnym pirolizom prirodnogo gaza [Hierarchical system of fuzzy regulation of acetylene production process by oxidative pyrolysis of natural gas]. Vestnik Astrahanskogo gosudarstvennogo tekhnicheskogo universiteta. Seriya: Upravlenie, vychislitelnaya tekhnika i informatika [Proceedings of Astrakhan State Technical University. Series: Management, computer science and informatics]. 1:7-17.
4. Ryabchikov, M. Yu., E. S. Ryabchikova, S. A. Filippov. 2021. Sistema upravleniya temperaturoj para posle paroperegrevatelnoj ustanovki s primeneniem nechetkoj logiki dlya uprezhdayushchej kompensacii vozmushchenij [A fuzzy logic-based system for controlling the temperature of steam exiting a superheater for the purpose of preemptive perturbation compensation]. Mekhatronika, avtomatizaciya, upravlenie. 22(4):181-190
5. Nifantov, V. M. 2019. Diagnostika, ocenka i prognozirovanie tekhnicheskogo sostoyaniya tekhnologicheskogo oborudovaniya pri pomoshchi nechetkoj ekspertnoj sistemy v centralizovannyh sistemah tekhnicheskogo obsluzhivaniya i remonta [Diagnostics, assessment and forecasting of technical condition of technological equipmentby using fuzzy expert system in centralized maintenance and repair systems]. Trudy Kolskogo nauchnogo centra RAN [Transactions Kola Science Centre]. 10(9):187-197.
6. Ledeneva, T.M., S.L. Podvalny, R.K. Stryukov, S.V. Degtyarev. 2016. Nechetkoe modelirovanie medicinskih ekspertnyh sistem [Fuzzy modelling medical expert systems]. Biomedicinskaya radioelektronika [Biomedicine radioengineering]. 9:16-24.
7. Dubenko, Yu. V., E. E. Dyshkant. 2018. Nechetkaya sistema opredeleniya optimalnyh metodov dlya prognozirovaniya parametrov slozhnyh tekhnicheskih sistem [Fuzzy system for determining optimal methods to forecast parameters of complex technical systems]. Izvestiya vysshih uchebnyh zavedenij. Povolzhskij region. Tekhnicheskie nauki [University proceedings. Volga region. Technical sciences]. 47(3):58-69.
8. Hoang, T. -M., N. -H. Tran, V. -L. Thai, D. -L. Nguyen and N. -H. Nguyen. 2022. An efficient IDS using FIS to detect DDoS in IoT networks. 9th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, Vietnam. 193-198.
9. Dikarev, P. V., A. A. Shilin, S. Yu. Yudin. 2022. Sistema raspoznavaniya avarijnyh rezhimov vozdushnyh linij elektroperedachi s ispolzovaniem nechetkoj logiki [System for recognition of emergency modes of overhead power lines using fuzzy logic]. Energo- i resursosberezhenie: promyshlennost i transport [Energy and resource saving: industry and transport]. 38(1):6-12.
10. Tutygin, V. S., B. H. M. A. Al Vindi, I. A. Ryabcev. Sistema raspoznavaniya boleznej rastenij po izobrazheniyam listev na osnove nechetkoj logiki i nejronnyh setej [System of recognition of plant diseases on leaves images on the basis of fuzzy logic and neural network]. Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Estestvennye i tekhnicheskie nauki [Modern Science: actual problems of theory and practice. Series "Natural and Technical Sciences]. 3:107-115.
11. Piegat, 2001. A. Fuzzy Modeling and Control. Physica-Verlag Heidelberg. 728 p.
12. Preuss, H. P., Tresp, V. 1994. Neuro-fuzzy. Automatisierungstechnische Praxis. 36(5):10-24.
13. Luukka, P. 2011. A new non-linear fuzzy robust PCA Algorithm and similarity classifier in Classification of medical data sets. International Journal of Fuzzy Systems. 13:153-162.
14. Sergienko M.A. 2008. Metody proektirovaniya nechetkoj bazy znanij [Designing methods of hierarchical knowledge base]. Vestnik Voronezhskogo gosudarstvennogo universiteta. Seriya: sistemnyj analiz i informacionnye tekhnologii [Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies]. 2:67-71.
15. Bezdek, J. C., Ehrlich, R., Full, W. 1984. FCM: the Fuzzy c-means clustering algorithm. Computers and Geosciences. 10:191-203.
16. Krishnapuram, R., J. M. Keller. 1993. A possibilistic approach to clustering. Fuzzy Systems. 1(2):98-110.
17. Xu, Z., J. Chen, J. Wu. 2008. Clustering algorithm for intuitionistic fuzzy sets. Inf. Sci. 178:3775-3790.
18. Ji, Z., Q. Sun, Y. Xia, Q. Chen, D. Xia, D. D. Feng. 2012. Generalized rough fuzzy c-means algorithm for brain MR image segmentation. Computer methods and programs in biomedicine. 108(2):644-655.
19. Hwang, C., F. C.-H. Rhee. 2007. Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C -Means. IEEE Transactions on Fuzzy Systems. 15(1):107-120.
20. Das, S., H. K. Baruah. 2014. A new kernelized fuzzy Cmeans clustering algorithm with enhanced performance. International Journal of Research in Advent Technology. 2(6):43-51.
21. Dickerson, J. A., B. Kosko. 2004. Fuzzy function approximation with ellipsoidal rules. IEEE Transaction on fuzzy systems. August 2004. 26(4):542-560.
22. Grünbaum, B. 1963. Measures of symmetry for convex sets. Proc. Sympos. Pure Math., Providence, USA. 7:233-270.; Grünbaum, B. 1963. Borsuk’s problem and related questions. Proc. Sympos. Pure Math., Providence, USA. 7:271-284.
23. Chernoysko, F. L. 1988. Ocenivanie fasovogo sostoyaniya dinamicheskih system [Estimation of the phase state of dynamic systems]. Moscow: Science: The main editorial office of the physical and mathematical literature. 320 p.
24. Raposo, A. A. M., V. M. de Souza, L. R. A. G. Filho. 2021. A construction of the minimum volume ellipsoid containing a set of points using BRKGA metaheuristic. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics. 8(1):010339.
25. Sun, P., R. Freund. 2004. Computation of minimumvolume enclosing ellipsoids .Oper. Res. 52(5):690–706.
26. Kumar, P., E. Yildirim. 2005. Minimum-volume enclosing ellipsoids and core sets. Journal of Optimization Theory and Applications. 126(1):1–21.
27. Todd, M. J., E. A. Yıldırım. 2007. On Khachiyan’s algorithm for the computation of minimum volume enclosing ellipsoids. Discrete Appl. Math. 155:1731–1744.
28. Foucart, S., H. Rauhut. 2013. A Mathematical Introduction to Compressive Sensing. Birkhäuser New York, NY. 625 p.
29. Magnus, J. R., H. Neudecker. 1988. Matrix differential calculus with applications in statistics and econometrics. John Wiley & Sons. 504 p.
30. Ledeneva, T.M. 2022. New Family of Triangular Norms for Decreasing Generators in the Form of a Logarithm of a Linear Fractional Function. Fuzzy sets and systems. 427:37-54.
31. Ledeneva, T.M. 2020. Additive generators of fuzzy operations in the form of a Linear Fractional Function. Fuzzy sets and systems. 386:1-24.
 
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