RUS  ENG
Full version
JOURNALS // Upravlenie Bol'shimi Sistemami

UBS, 2018, Issue 74, Pages 6–22 (Mi ubs960)

Mamdani fuzzy inference system local tuning algorithm with the saving interpretation capability of inference rules
M. Golosovskiy

References

1. Bogomolov A. V., “Ispolzovanie lingvisticheskikh peremennykh i metodov obrabotki ekspertnoi informatsii dlya avtomatizirovannogo raspoznavaniya rannikh stadii narusheniya funktsionalnogo sostoyaniya cheloveka”, Informatsionnye tekhnologii, 2000, no. 8, 12–18
2. Borisov V. V., Kruglov V. V., Fedulov A. S., Nechëtkie modeli i seti, Goryachaya liniya-Telekom, M., 2007, 284 pp.
3. Golosovskii M. S., “Primenenie sistemy na osnove nechëtkoi logiki v zadachakh upravleniya proektami po razrabotke programmnogo obespecheniya”, Materialy Kh mezhdunarodnoi nauchnoi konferentsii «Innovatsionnoe razvitie obschestva: usloviya, protivorechiya, prioritety», ed. A.V. Semenov, 2014, 400–404
4. Zade L., Ponyatie lingvisticheskoi peremennoi i ego primenenie k prinyatiyu priblizhennykh reshenii, Mir, M., 1976, 167 pp.
5. Kruglov V. V., “Sravnenie algoritmov Mamdani i Sugeno v zadache approksimatsii funktsii”, Matematicheskaya morfologiya: elektronnyi matematicheskii i mediko-biologicheskii zhurnal, 2001, no. 4, 69–76
6. Kudinov Yu. I., Kelina A. Yu., “Metody sinteza i nastroiki nechetkikh PID regulyatorov Mamdani”, Informatsionnye tekhnologii, 2012, no. 6, prilozhenie, 32 pp.
7. Paklin N. B., Adaptivnye modeli nechetkogo vyvoda dlya identifikatsii nelineinykh zavisimostei v slozhnykh sistemakh, Avtoref. dis. kand. tekhn. nauk, Izhevsk, 2004, 20 pp.
8. Pegat A., Nechëtkoe modelirovanie i upravlenie, 2-e izdanie, BINOM. Laboratoriya znanii, M., 2013, 798 pp.
9. Silich V. A., Silich M. P., Aksenov S. V., “Algoritm postroeniya nechetkoi sistemy logicheskogo vyvoda Mamdani, osnovannyi na analize plotnosti obuchayuschikh primerov”, Doklady TUSUR, 2013, no. 3(29), 76–82
10. Shtovba S. D., Mazurenko V. V., Tylets R. O., “Infor-matsionnaya tekhnologiya nechetkoi identifikatsii dlya sinteza tochnykh, kompaktnykh i interpretabelnykh baz znanii”, Computer Sciences and Telecommunications, 2016, no. 1(47), 8–22
11. Gacto M., Alcala R., Herrera F., “Interpretability of lin-guistic fuzzy rule - based systems: An overview of interpretability measures”, Information Sciences, 181:20 (2011), 4340–4360
12. Gang F., Analysis and synthesis of fuzzy control systems: a model-based approach, Automation and control engineering, CRC Press, 2017, 299 pp.
13. Kosko B., “Fuzzy systems as universal aproximators”, IEEE Transactions on Computers, 43:11 (1994), 1329–1333
14. Kosko B., “Global stability of generalized additive fuzzy systems”, IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Reviews, 28:3 (1998), 441–452
15. Maistrou A. I., Bogomolov A. V., “Technology of auto-mated medical diagnostics using fuzzy linguistic variables and consensus ranking methods”, IFMBE Proc. of the World Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering. Cycle: “World Congress on Medical Physics and Biomedical Engineering: Diagnostic and Therapeutic Instrumentation, Clinical Engineering” (Munich, 2009), 38–41
16. Manentia F., Rossia F., Goryunov A., Dyadik A., Kozin K., Nadezhdin I., Mikhalevich S., “Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier”, Resource-Efficient Technologies, 1:1 (2015), 10–18
17. Miller G. A., “The magical number seven plus or minus two: some limits on our capacity for processing information”, The Psychological Review, 1956, no. 63, 81–97


© Steklov Math. Inst. of RAS, 2025