Chemical Technology, Control and Management


Methods and simplified computational schemes for optimizing data processing for systems operating in conditions of limited a priori information, changes in the characteristics of external influences, uncertainty of parameters have been developed. To describe a non-stationary object, non-linear identification models are considered, constraints, input conditions for obtaining possible values of output variables are defined. An approach aimed at using identification technologies based on generalization of capabilities of dynamic models, neural networks (NN), mechanisms for regulating variable computing schemes of structural network components, as well as learning algorithms of the NN is proposed. A generalized algorithm for learning NN based on the synthesis of the mechanisms of probabilistic, heuristic and stochastic search, the formation of a set of training data, regulation of the values of variables of linear and nonlinear dependences of inputs and outputs is developed. The efficiency of the implemented data processing software complex is analyzed by various methods, algorithms, speed, graphs and requirements, and also estimates of the stability of the generalized learning algorithm of NN and the minimum mean-square error are obtained.

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1. E.S.Kuznecov, Upravlenie tehnicheskimi sistemami [Management of technical systems]. Moskva: Nauka, 2001, 535 p. (in Russian).

2. I.B.YAdy'kin, V.M.SHuyskiy, T.A.Ovsepyan, Adaptivnoe upravlenie neprery'vny'mi processami [Adaptive management of continuous processes]. Moskva: E`nergoatomizdat, 1985, 240 p. (in Russian).

3. I.V.Miroshnik, V.O.Nikiforov, A.L.Fradkov, Nelineynoe i adaptivnoe upravlenie slojny'mi dinamicheskimi sistemami [Nonlinear and adaptive control of complex dynamic systems]. Moskva: Nauka, 2000, 314 p. (in Russian).

4. N.N.Karabutov, Adaptivnaya identifikaciya sistem. Informacionny'y sintez [Adaptive system identification. Information synthesis]. Moskva: Kom Kniga, 2006, 384 p. (in Russian).

5. A.M.Vul'fin, A.I.Frid, Neyrosetevaya model' analiza tehnologicheskih vremenny'h ryadov v ramkah metodologii Data Mining [Neural network model for analyzing technological time series in the framework of the methodology Data Mining], Informacionno-upravlyayusch'ie sistemy', no. 5, pp. 31-38, 2011 (in Russian).

6. A.F.CHipiga, R.A.Voronkin, Obuchenie iskusstvenny'h neyronny'h setey putem sovmestnogo ispol'zovaniya metodov lokal'noy optimizacii i geneticheskih algoritmov [Learning artificial neural networks ways to share local optimization methods and genetic algorithms], Izvestiya TRTU, vol. 33, no. 4, pp. 172-174, 2007 (in Russian).

7. N.G.YArushkina, Osnovy' nechetkih i gibridny'h system [Basics of fuzzy and hybrid systems]. Moskva: Finansy' i statistika, 2004, 320 p. (in Russian).

8. I.I.Jumanov, A.B.Islomov, “Optimizaciya obrabotki izobrajeniy mikroob`ektov na osnove rekurrentnogo obucheniya neyronnoy seti i implikativnogo otbora informativny'h priznakov” [Optimization of micro-lens image processing based on recurrent neural network training and implicative selection of information features], Nauka i mir, no. 5(33), pp. 78-81 2016 (in Russian).

9. I.I.Jumanov, S.M.Holmonov, “Optimizaciya identifikacii nestacionarny'h ob`ektov na osnove segmentacii vremenny'h ryadov i nastroyki parametrov neyronnoy seti” [Optimization of non-stationary object identification based on time series segmentation and neural network parameter settings], Vestnik TUIT, no. 4(40)/2016, pp. 32-41, 2016 (in Russian).

10. O.I.Djumanov, “Metody' adaptivnoy obrabotki danny'h na osnove mehanizmov gibridnoy identifikacii s nastroykoy parametrov modeley nestacionarny'h ob`ektov” [Method of adaptive data processing based on hybrid identification mechanisms with setting parameters of models of non-stationary objects], Problemy' informatiki, no. 2(31), pp. 13-21, 2016 (in Russian).

11. O.I.Djumanov, S.M.Kholmonov, “The modified model of training of neural networks in computer industrial systems with modules for nonstationary objects images processing”, Journal of Korea Multimedia Society, no. 5, pp. 54-58, 2016.

12. S.M.Holmonov, “Adaptivnaya obrabotka danny'h nestacionarnogo processa na osnove modeley nechetkoy logiki i neyronnoy seti” [Adaptive data processing of a non-stationary process based on fuzzy logic and neural network models], Himicheskaya tehnologiya. Kontrol' i upravlenie, no. 5, pp. 90-96, 2010 (in Russian).

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