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The problem of optimization of the fuzzy control model of the drying unit in the cotton ginning industry operating under conditions of a priori uncertainty is considered. As an example, the process of temperature control in a drying unit is considered. As methods of optimization, it is proposed to use probabilistic methods. As an optimization parameter, the fuzzy model membership functions are used. A comparative analysis of the results obtained on the basis of simulation modeling before and after optimization is carried out. 

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI10-01-2020
  • Количество прочтений164
  • Дата публикации19-10-2018
  • Язык статьиIngliz
  • Страницы71-74
English

The problem of optimization of the fuzzy control model of the drying unit in the cotton ginning industry operating under conditions of a priori uncertainty is considered. As an example, the process of temperature control in a drying unit is considered. As methods of optimization, it is proposed to use probabilistic methods. As an optimization parameter, the fuzzy model membership functions are used. A comparative analysis of the results obtained on the basis of simulation modeling before and after optimization is carried out. 

Имя автора Должность Наименование организации
1 Siddikov I.K. Tashkent State Technical University, Department of Information processing and control system, University st. 2, 100095 TDTU
2 Yunusova S.T. Tashkent State Technical University, Department of Information processing and control system, University st. 2, 100095 TDTU
3 Izmaylova R.N. Tashkent State Technical University, Department of Information processing and control system, University st. 2, 100095 TDTU
Название ссылки
1 1. Sinyavskaya E.D. Analysis of the accuracy of the fuzzy model and optimization of its parameters by the example of temperature control in a baking chamber. Materials of the II All-Russian Scientific and Practical Conference "Youth, Science, Innovation", Grozny 20133.с. 95-100. 2. Alp Yanar T., Akyurek Z., 2011 Fuzzy model tuning using simulated annealing, Expert Systems with Applications.№38: 8159-8169. 3. Shtovba S.D. Ensuring the accuracy and transparency of the fuzzy model of Mamdani in training on experimental data // Problems of Management and Informatics.2007.№4.с.1-13. 4. Shtovba S.D. Designing control systems Fuzzy Logic Toolbox. URL: http: // matlab. exponenta.ru/. 5. Gmurman V.E. Probability theory and mathematical statistics M: Vshshaya shkola, 2003.479p. 6. Baudrita C., Duboisb D., Perrota N., 2008 Representing parametric probabilistic models tainted with imprecision. Fuzzy Sets and Systems.159: 1913: 1928. 7. NR Yusupbekov., RAAliyev., RR Aliyev., A.Yusupbekov. Intellectual systems of management and decision making /, - Tashkent: State scientific publisher Uzbekiston Milli Encyclopedia ", 2014.- 490 p. 8. Siddikov I.H., Izmaylova R.N., KarimovSh.S. LOGICGRAPHIC Model of Monitoring of the Technological Statuses of the Petrochemical Equipment. International Journal of Advanced Research in Science, Engineering and Technology Vol. 4, Issue 3, March 2017.
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