Optimization of Data Processing of Non-Stationary Processes Based on Setting the Parameters of Fuzzy Models
Keywords:
data processing, fuzzy sets, non-stationary object, genetic algorithm, optimization, technological process
Abstract
Scientific and methodological foundations, methods, models and algorithms for processing data of non-stationary objects based on the mechanisms for setting the parameters of fuzzy sets using genetic algorithms have been developed. The processes of transformation of input data into output data are modeled based on the use of the properties of fuzzy sets and functional connections of inputs and outputs. Methods of intellectual analysis and data processing are implemented for forecasting in automated process control systems.
References
1. Сhеn, S. М. Forecasting enrollments based on fuzzy time series / S.М.Chen // Fuzzy Sets Systems. - 1996. - V. 81. - № 3. - pp. 311-319.
2. Дворецкий, С. И. (2008). Основы математического моделирования и оптимизации процессов и систем очистки и регенерации воздуха.
3. Mudi, R. K., & Pal, N. R. (1999). A robust self-tuning scheme for PI-and PD-type fuzzy controllers. IEEE Transactions on fuzzy systems, 7(1), 2-16.
4. Jumanov, I. I., & Xolmonov, S. M. (2021, February). Optimization of identification of non-stationary objects due to information properties and features of models. In IOP Conference Series: Materials Science and Engineering (Vol. 1047, No. 1, p. 012064). IOP Publishing. DOI:10.1088/1757-899X/1047/1/012064
5. Jumanov, I. I., & Kholmonov, S. M. (2020). OPTIMIZATION OF IDENTIFICATION UNDER THE CONDITIONS OF LOW RELIABILITY OF INFORMATION AND PARAMETRIC UNCERTAINTY OF NON-STATIONARY OBJECTS. Chemical Technology, Control and Management, 2020(5), 104-112.
6. Jumanov, I. I., & Karshiev, K. B. (2019). Analysis of efficiency of software tools optimizing the information reliability of electronic documents in automated control systems. Chemical Technology, Control and Management, 2019(2), 57-66. https://doi.org/10.34920/2019.2.57-66
7. Ibragimovich, J. I., Isroilovich, D. O., & Maxmudovich, X. S. (2020, November). Effective recognition of pollen grains based on parametric adaptation of the image identification model. In 2020 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-5). IEEE. DOI: 10.1109/ICISCT50599.2020.9351486
8. Djumanov, O. I., Kholmonov, S. M., & Ganiev, J. M. (2021). OPTIMIZATION OF DATA PROCESSING BASED ON NEURAL NETWORKS AND PROPERTIES OF NON-STATIONARY OBJECTS. Theoretical & Applied Science, (4), 165-168.
9. Джуманов, О. И. (2016). Методы адаптивной обработки данных наоснове механизмов гибридной идентификации с настройкой параметров моделей нестационарных объектов. Проблемы информатики, (2 (31)), 13-20.
10. Djumanov O.I., Kholmonov S.M., Ganiev J.M. Optimization of data processing of non-stationary objects with neural network learning regulators // International Journal of Advanced Research in Science, Engineering and Technology, India, Vol. 8, Issue 2, February 2021, pp. 16936-16939
11. Israilovich, D. O., Makhmudovich, K. S., & Uktomovich, Y. F. (2021). Increasing The Credibility Of Forecasting Random Time Series Based On Fuzzy Inference Algorithms. International Journal of Progressive Sciences and Technologies, 26(1), 12-15.
12. Ахатов, А. Р., & Холмонов, С. М. (2018). Повышение достоверности передачи и обработки временного ряда путем фильтрации нестационарных составляющих. Проблемы вычислительной и прикладной математики, (1), 90-99.
13. Jumanov I.I., Kholmonov S.M. Porogoviy kontrol tochnosti neprerivnoy informasii v avtomatizirovannix sistemax upravleniya texnologicheskimi prosessami // V mejdunarodnoй nauchno-prakticheskoй konferensii «Nauka i obrazovaniye v sovremennom mire: vizovi XXI veka» Tezisi dokladov, NUR-SULTAN, Kazaxstan, 10-12 dekabrya 2019 g. – pp. 308-313
14. Djumanov O.I., Kholmonov S.M., Ulugmurodov Sh. Povisheniye tochnosti obrabotki informasii neprerivnix obyektov s mexanizmom kontrolya po modelyam regressii // V mejdunarodnoy nauchno-prakticheskoy konferensii «Nauka i obrazovaniye v sovremennom mire: vizovi XXI veka» Tezisi dokladov, NUR-SULTAN, Kazaxstan, 10-12 dekabrya 2019 g. – pp. 313-318
2. Дворецкий, С. И. (2008). Основы математического моделирования и оптимизации процессов и систем очистки и регенерации воздуха.
3. Mudi, R. K., & Pal, N. R. (1999). A robust self-tuning scheme for PI-and PD-type fuzzy controllers. IEEE Transactions on fuzzy systems, 7(1), 2-16.
4. Jumanov, I. I., & Xolmonov, S. M. (2021, February). Optimization of identification of non-stationary objects due to information properties and features of models. In IOP Conference Series: Materials Science and Engineering (Vol. 1047, No. 1, p. 012064). IOP Publishing. DOI:10.1088/1757-899X/1047/1/012064
5. Jumanov, I. I., & Kholmonov, S. M. (2020). OPTIMIZATION OF IDENTIFICATION UNDER THE CONDITIONS OF LOW RELIABILITY OF INFORMATION AND PARAMETRIC UNCERTAINTY OF NON-STATIONARY OBJECTS. Chemical Technology, Control and Management, 2020(5), 104-112.
6. Jumanov, I. I., & Karshiev, K. B. (2019). Analysis of efficiency of software tools optimizing the information reliability of electronic documents in automated control systems. Chemical Technology, Control and Management, 2019(2), 57-66. https://doi.org/10.34920/2019.2.57-66
7. Ibragimovich, J. I., Isroilovich, D. O., & Maxmudovich, X. S. (2020, November). Effective recognition of pollen grains based on parametric adaptation of the image identification model. In 2020 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-5). IEEE. DOI: 10.1109/ICISCT50599.2020.9351486
8. Djumanov, O. I., Kholmonov, S. M., & Ganiev, J. M. (2021). OPTIMIZATION OF DATA PROCESSING BASED ON NEURAL NETWORKS AND PROPERTIES OF NON-STATIONARY OBJECTS. Theoretical & Applied Science, (4), 165-168.
9. Джуманов, О. И. (2016). Методы адаптивной обработки данных наоснове механизмов гибридной идентификации с настройкой параметров моделей нестационарных объектов. Проблемы информатики, (2 (31)), 13-20.
10. Djumanov O.I., Kholmonov S.M., Ganiev J.M. Optimization of data processing of non-stationary objects with neural network learning regulators // International Journal of Advanced Research in Science, Engineering and Technology, India, Vol. 8, Issue 2, February 2021, pp. 16936-16939
11. Israilovich, D. O., Makhmudovich, K. S., & Uktomovich, Y. F. (2021). Increasing The Credibility Of Forecasting Random Time Series Based On Fuzzy Inference Algorithms. International Journal of Progressive Sciences and Technologies, 26(1), 12-15.
12. Ахатов, А. Р., & Холмонов, С. М. (2018). Повышение достоверности передачи и обработки временного ряда путем фильтрации нестационарных составляющих. Проблемы вычислительной и прикладной математики, (1), 90-99.
13. Jumanov I.I., Kholmonov S.M. Porogoviy kontrol tochnosti neprerivnoy informasii v avtomatizirovannix sistemax upravleniya texnologicheskimi prosessami // V mejdunarodnoй nauchno-prakticheskoй konferensii «Nauka i obrazovaniye v sovremennom mire: vizovi XXI veka» Tezisi dokladov, NUR-SULTAN, Kazaxstan, 10-12 dekabrya 2019 g. – pp. 308-313
14. Djumanov O.I., Kholmonov S.M., Ulugmurodov Sh. Povisheniye tochnosti obrabotki informasii neprerivnix obyektov s mexanizmom kontrolya po modelyam regressii // V mejdunarodnoy nauchno-prakticheskoy konferensii «Nauka i obrazovaniye v sovremennom mire: vizovi XXI veka» Tezisi dokladov, NUR-SULTAN, Kazaxstan, 10-12 dekabrya 2019 g. – pp. 313-318
Published
2022-02-16
How to Cite
Makhmudovich, K. S., & qizi, M. G. M. (2022). Optimization of Data Processing of Non-Stationary Processes Based on Setting the Parameters of Fuzzy Models. International Journal of Human Computing Studies, 4(2), 36-42. https://doi.org/10.31149/ijhcs.v4i2.2723
Section
Articles
3.png)
