Abstract
The issue of identifying recirculation systems with a specific control loop, in which information and control information about various technological processes circulate, is being considered. A methodology for identifying recirculation systems has been developed, based on algorithms for structural identification of the model based on the results of assessing the sensitivity of the optimum of control parameters and a fuzzy inference algorithm based on production rules that contribute to solving the problem of parametric identification.
First Page
43
Last Page
48
References
- Pegat, A. (2009). Nechetkoe modelirovanie i upravlenie [Fuzzy Modeling and Control]. M.: BINOM, 798 p. (in Russian).
- Izerman, R. (1984). Sifrovie sistemi upravleniya [Digital control systems]. M.: Mir, 541 p. (in Russian).
- Semenov, A.D., Artamonov, D.V., Bryuxachev, A.V. (2003). Identifikasiya ob'ektov upravleniya [Identification of control objects]. Penza: Izd. Penz. gos, un-t., 211 p. (in Russian).
- Bobsov, A.A., Kremlev, A.S., Pirkin, A.A. (2011). Kompensasiya garmonicheskogo vozmusheniya dlya parametricheskogo i funksional'no neopredelennogo nelineynogo ob'ekta [Compensation of harmonic disturbance for parametric and functionally uncertain nonlinear object]. Avtomatika i telemexanika, 1, 121-129. (in Russian).
- Igambediev, X.Z., Mamirov, U.F. (2018). Regulyarizovannie algoritmi identifikasii neopredelennix dinamicheskix ob'ektov upravleniya [Regularized algorithms for identifying uncertain dynamic control objects]. Jurnal «Vestnik Tash GTU». 2, 16-21. (in Russian).
- Igamberdiev, H.Z., Yusupbekov, A.N., Zaripov, O.O., Sevinov, J.V. (2017). Algorithms of adaptive identification of uncertain operated objects in dynamical models. Procedia Computer Science, 120, 854 - 861.
- Ismailov, M.A., Kaipberganov, B.T., Fayzullaev, B.A. (2022). Intellektual'nie sistemi diagnostiki proizvodstvennix ob'ektov [Intelligent diagnostic systems for production facilities]. NIU “TNIMMSX”. 2022, 114 p.
- Kuksa, P.P. Sintez i optimizasiya nelokal'nix interpretiruemix lingvisticheskix neyro-nechetkix modeley [Synthesis and optimization of non-local interpretable linguistic neuro-fuzzy models]. E-mail: pkouxa@yahoo.com www.geocities.com/pkouxa. (in Russian).
- Muxamedieva, D.T., Primova, X.A., Xasanov, U.U. (2016). Neyro-nechetkiy algoritm identifikasii i nastroyki parametrov sistem nechetkogo vivoda [Neuro-fuzzy algorithm for identifying and setting parameters of fuzzy inference systems]. Uzbek Journal of the problems of informatics and energetic. 3, 21-27. (in Russian).
- Boyarinov, A.I., Kafarov, V.V. (1969). Metodi optimizasii v ximicheskoy texnologii [Optimization methods in chemical technology]. M.: Izd. «Ximiya», 564 p. (in Russian).
- Shtovba, S.D. Vvedenie v teoriyu nechetkix mnojestv i nechetkuyu logiku [Introduction to fuzzy set theory and fuzzy logic]. http://www.matlab.exponenta.ru. (in Russian).
- Kudinov, Yu.I., Kudinov, I.Yu., Baykov, S.V.. Identifikasiya i obuchenie v nechetkix sistemax [Identification and learning in fuzzy systems]. Avtomatizasiya i informatika. (in Russian).
- Xodashinskiy, I.A. (2008). Texnologiya identifikasii nechetkix modeley tipa singlton i Mamdani [Identification technology for singleton and mamdani type fuzzy models]. Tr. VII mejdunarodnoy konferensii. Identifikasiya sistem i zadach upravleniya “SICPRO” 081. Institut problem upravleniya. 137-163. (in Russian).
Recommended Citation
Igamberdiev, Khusan and Fozilova, Madina Mirxalilovma
(2024)
"STRUCTURAL AND PARAMETRIC IDENTIFICATION OF RECYCLING SYSTEMS,"
Chemical Technology, Control and Management: Vol. 2024:
Iss.
3, Article 6.
DOI: https://doi.org/10.59048/2181-1105.1593
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