Open Access
Issue
E3S Web of Conf.
Volume 365, 2023
IV International Scientific Conference “Construction Mechanics, Hydraulics and Water Resources Engineering” (CONMECHYDRO - 2022)
Article Number 01013
Number of page(s) 11
Section Ecology, Hydropower Engineering and Modeling of Physical Processes
DOI https://doi.org/10.1051/e3sconf/202336501013
Published online 30 January 2023
  1. Kabildjanov A.S., Okhunboboyeva Ch.Z. Intelligent support acceptance solutions in multicriteria tasks optimization in uncertainty conditions. International Journal of Research and Development (IJRD), 4(4), pp. 221–228 (2019) [Google Scholar]
  2. Kabildzhanov A.S., Okhunbabaeva Ch., Avazbaev A.A. Method for selecting the optimal values of the parameters of reclamation equipment in conditions of multi-criteria. Journal “Bulletin of Agrarian Science of Uzbekistan”, Tashkent State Agrarian University, 4, pp. 98–105, (2017) [Google Scholar]
  3. Kotenko A.P., Pshenina D.A. Multi-criteria optimization based on the regression equation systems identification. International Conference Information Technology and Nanotechnology, Samara State Aerospace University Samara, Russian Federation, CEUR Workshop Proceedings, 1638: pp. 593–599 (2016) DOI: 10.18287/1613-0073-2016-1638-593-599 [Google Scholar]
  4. Podinovsky V.V. Ideas and methods of the theory of the importance of criteria in multi-criteria decision-making problems. Moscow, Nauka, p. 103 (2019) [Google Scholar]
  5. Dragan Pamucar. Uncertain Multi-Criteria Optimization Problems. Printed Edition of the Special Issue Published in Journal Symmetry, Published by MDPI, p. 764 (2021). https://doi.org/10.3390/books978-3-0365-1573-1 [Google Scholar]
  6. Tikhonov A.N., Goncharsky A.V., Stepanov V.V., Yagola A.G. Regularizing algorithms and a priori information, Moscow, Nauka, p. 230 1990. [Google Scholar]
  7. Sumin M.I. Regularization method A.N. Tikhonov for solution of optimization tasks: Teaching aid. Nizhny Novgorod: Below-City State University, p. 35 (2016) [Google Scholar]
  8. Chernorutsky I.G. Optimal parametric synthesis. Electrical devices and systems. – Leningrad: Energoizdat, p. 110 (1987) [Google Scholar]
  9. Karpenko A.P., Moor D.T., Mukhlisullina D.T. Neural network, fuzzy and neuro-fuzzy approximation in the problem of multi-criteria optimization. Neuroinformatics-2011: Tez. etc. 3rd All-Russian Scientific and Technical Conference. 1, pp. 60–69 (2011) [Google Scholar]
  10. Kabildjanov A., Bozorov E., Okhunboboyeva Ch., Tuhtaeva G. Intellectualization of Decision Making Support in Tasks of Optimization of Complex Technical Systems based on Anfis Neuro-Fuzzy Network. Annals of the Romanian Society for Cell Biology, 25(1), pp. 6967 – 6979 (2021) [Google Scholar]
  11. Mukhlisullina D.T., Moor D.A., Karpenko A.P. Multicriteria optimization based on fuzzy approximation of the decision maker’s preference function. Electronic scientific and technical edition: Science and Education. № 1. p. 6 (2010). URL: http://technomag.edu.ru/doc/135375.html. [Google Scholar]
  12. Kabildzhanov A.S. Fuzzy Approximation in the Problems of Optimal Parametric Synthesis of Technical Objects. Journal »Problems of Informatics and Energy», Tashkent, 5, pp. 23–32 (2016) [Google Scholar]
  13. Kruglov V.V., Borisov V.V. Artificial neural networks. Theory and practice. – 2nd ed., stereotype. – M.: Hotline -Telecom, p. 382, (2002) [Google Scholar]
  14. Kabildjanov A.S., Bozorov E.O., Okhunboboeva Ch.Z. Optimizationand Simitation of the Process Electro Impulse Treatment of Plants/ Exploring Innovation. Journal International Journal of Engineering and Advanced Technology (IJEAT), 9(1), pp. 4850 – 4853 (2019) [Google Scholar]
  15. Okhunboeva Ch.Z., Kabiljanov A.S.Features of the application of regression models in the problems of multi-criteria parametric optimization of agricultural facilities. Journal »Problems of Informatics and Energy», Tashkent, 5, pp. 11–19 (2021) [Google Scholar]
  16. Carlos A. Reyes-García, Alejandro A. Torres-García. Chapter 8 – Fuzzy logic and fuzzy systems. Biosignal Processing and Classification Using Computational Learning and Intelligence, Academic Press, pp. 153–176, (2022), doi.org/10.1016/B978-0–12-820125–1.00020–8 [CrossRef] [Google Scholar]
  17. Chao Xiao, Haiyang Zou, Junliang Fan, Fucang Zhang, Yi Li, Shikun Sun, Alim Pulatov, Optimizing irrigation amount and fertilization rate of drip-fertigated spring maize in northwest China based on multi-level fuzzy comprehensive evaluation model. Journal Agricultural Water Management, 257, (2021), https://doi.org/10.1016/j.agwat.2021.107157. [Google Scholar]
  18. Goldstein A.L. Optimization in the MATLAB environment: textbook. Allowance. A.L. Goldstein. – Perm: Publishing House of Perm. nat. research polytechnic un-ta, p. 192 (2015) [Google Scholar]
  19. Viviana Consonni, Giacomo Baccolo, Fabio Gosetti, Roberto Todeschini, Davide Ballabio. A MATLAB toolbox for multivariate regression coupled with variable selection. Journal Chemometrics and Intelligent Laboratory Systems, 213, (2021), doi.org/10.1016/j.chemolab.2021.104313. [Google Scholar]
  20. Aranovsky S.V., Gritsenko P.A., Tools for the numerical solution of optimization problems. – St. Petersburg: ITMO University, p. 30 (2016) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.