SELECTING THE OPTIMAL COMBINATION OF MEMBERSHIP FUNCTIONS FOR QUERIES BASED ON FUZZY LOGIC BY EVALUATING THE SENSITIVITY

Sanjar Sobirovich Kenjaev; Akmal Rustamovich Akhatov; Ma’ruf Ruziqulovich Tojiev

Authors

Sanjar Sobirovich Kenjaev Samarkand State University
Akmal Rustamovich Akhatov Samarkand State University
Ma’ruf Ruziqulovich Tojiev Samarkand State University

Keywords:

linguistic variable, membership function, fuzzy logic

Abstract

Efficient request distribution is one of the important factors for the stable and uninterrupted operation of modern information systems. In the context of increasing cloud computing, real-time services and multi-user environments, traditional distribution algorithms (e.g. Round Robin, Least Connection, Weighted Response Time) are losing their effectiveness due to insufficient consideration of uncertainty and dynamic changes in server state. In this context, there is a growing need for decision-making systems based on adaptive and state-sensitive approaches.
Fuzzy logic-based models have a significant advantage in representing uncertainties in real-world environments, especially due to the ability to estimate system parameters through linguistic variables. However, the effectiveness of these systems directly depends on the shape and parameters of the membership functions, i.e. their semantic relevance and sensitivity. Using membership functions of the same shape for each linguistic value (for example, only a triangle) can reduce the accuracy of the system's decision-making.

References

1. Balancing The Computing Load In Modern Web Applications K.A. Krinitsyn, V.Yu. Noskov, 2012. 227-231 pages Heat Engineering and Informatics in Education, Science and Production (TIM'2012) elar.urfu.ru

2. Review Of Problems And States Of Cloud Computing And Services O.S. Kovalenko, V.M. Kureichik Journal News of the Southern Federal University. Technical sciences 146-153 p. 2012

3. J. Dombi, Membership function as an evaluation Reseach Group of Theory of Automata 6720 Szeged, Somogyi u. 7, Hungary https://doi.org/10.1016/0165-0114(90)90014-W

4. Jie Zhang, Julian Morris Process modelling and fault diagnosis using fuzzy neural networks doi.org/10.1016/0165-0114(95)00295-2

5. Te-Hui Wu a b, Yu-I. Huang a, Jiunn-Ming Chen Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture doi.org/10.1016/j.aquaeng.2015.02.001

6. А. Axatov, M. Nurmamatov, F. Nazarov, and Sh. Sariyev, “Genetic algorithm application technology in multi-parameter optimization problems,” AIP Conf. Proc., vol. 3244, art. no. 030025, 2024, doi: 10.1063/5.0242074

7. Raximov, Nodir, Ma’ruf Tojiyev, and Usanitdin Xafizadionov. " Methods for assessing the level of knowledge of learners based on normalization of indicators of information resource use." Digital Transformation and artificial intelligence 2.5 (2024): 91-96.

8. B. Z. Parmanov, F. M. Nazarov and M. R. Tojiyev, "Development of an Algorithm for Intelligent Analysis of Data From a Microprocessor-Based Portable Spectrophotometer," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 664-669, doi: 10.1109/SmartIndustryCon65166.2025.10985982.

9. D. Khasanov, M. Tojiyev and O. Primqulov, "Gradient descent in machine learning," 2021 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2021, pp. 1-3, doi: 10.1109/ICISCT52966.2021.9670169.

10. S.S.Kenjaev, A.E.Rashidov, “File storage methods and algorithms for optimal management of various types of data” "Descendants of Al-Farghani" electronic scientific journal of Fergana branch of TATU named after Muhammad al-Khorazmi. Vol: 1 | Iss: 3 | 2024, DOI: 10.5281/zenodo.13954911