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Chemical Technology, Control and Management

Abstract

The tasks of taking into account the sensitivity of fuzzy modeling based on the mechanisms for determining the range of elements of randomly chosen time series and the correction of the parameters of the functions of the accessories of linguistic variables, the use of the data property and the specific features of objects, the database and the knowledge base are solved. Computational schemes of dynamic identification based on polynomial models, nonlinear filters with fuzzy variable adapters are constructed. The effectiveness of generalized algorithms of fuzzy identification of randomly time series (RTS) is proved by comparison with the values of the characteristics of modal examples.

First Page

119

Last Page

122

DOI

https://doi.org/10.34920/2018.4-5.119-122

References

  1. YU.N.Minaev, O.Yu.Filimonova, B.Lies, “Metody' i algoritmy' resheniya zadach identifikatsii i prognozirovaniya v usloviyax neopredelennosti v neyrosetevom logicheskom bazise” [Methods and algorithms for solving identification and forecasting problems under uncertainty in a neural network logical basis]. Moscow: Goryachaya liniya Telekom, pp. 12-18, 2003. (in Russian)
  2. B.Q.Huang, T.Rashid, M.T.Kechadi, “Multi Context Recurrent Neural Network for Time Series Applications International”, Journal of Computational Intelligence, vol.3, no. 1, pp. 1304-1386, 2006.
  3. G.G.Shpiro, “Prognozirovanie xaoticheskix vremenny'x ryadov s geneticheskim algoritmom” [Predicting chaotic time series with a genetic algorithm], Physical Review E. vol. 55, no. 3, pp. 2557-2568, 1997. (in Russian)
  4. I.I.Jumanov, “Optimizatsiya obrabotki danny'x nestatsionarny'x ob’ektov na osnove nechetkix modeley identifikatsii s nastroykoy parametrov” [Optimization of data processing of non-stationary objects or the basis of fuzzy identification models with parameter settings], Jurnal «Vestnik TUIT». Tashkent, no. 1(41)2017, pp. 34-47, 2017. (in Russian)
  5. K.T.Leondesa, “Fil'tratsiya i stoxasticheskoe upravlenie v dinamicheskix sistemax” [Filtering and statistical control in dynamic systems], Pod red. Per. s angl., Moscow: Mir, 1980, 407 p. (in Russian)
  6. I.N.Sinitsy'n, “Fil'try' Kalmana i Pugacheva Izdvo” [Filters of Kalman and Pugachev]: Logos, 2006, 640 p. (in Russian)
  7. S.B.Pel'sverger, “Algoritmicheskoe obespechenie protsessov otsenivaniya v dinamicheskix sistemax v usloviyax neopredelennosti”[ Algorithmic support of estimation processes in dynamic systems under uncertainty], Moscow: Nauka, 2004, 116 p.
  8. A.G.Bashkirov, “Entropiya Ren'i kak statisticheskaya entropiya dlya slojny'x system” [Renyi entropy as a statistical entropy for a complex system], Teoreticheskaya i matematicheskaya fizika. vol. 149, no. 2, Moscow: Nauka, pp. 299-317, 2006. (in Russian)

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