Tel: +998901234567
Search

No. 11 (2025)
Marketing

Articles

NEURO-RAWSHAN CLASSIFICATION MODEL OF FORMATION OF MULTIFACTOR INDICATORS OF POPULATION'S STANDARD OF LIVING

Published 2025-11-29

Authors
Mirzayev Shoxrux Normurod oʻgʻli
Affiliation
Karshi State Technical University is an independent researcher
PDF (Uzbek) Google Scholar Google Scholar

Abstract

This paper examines the development of a multifactorial system of indicators for assessing and improving a personʼs standard of living based on a neural network classification model. The study developed an integrated indicator system combining demographic, economic, social, and infrastructure indicators. Furthermore, artificial neural networks and fuzzy logic elements were combined to account for uncertainty, systematicity, and complex nonlinear relationships between indicators.

Keywords

standard of living, multifactor indicators, fuzzy classification, fuzzy clustering, fuzzy C -means, neuro -fuzzy classification, neuro -fuzzy model

References

  1. Nazarov Sh.R. – “Koʻp omilli modellashtirish asoslari” 2019 y.
  2. World Bank. (2021). Measuring poverty. Retrieved from https://www.worldbank.org/en/topic/measuringpoverty
  3. Sen, A. (1999). Development as freedom. Oxford: Oxford University Press.
  4. D. Pamucar, D. Bozanic, A. Puska, and D. Marinkovic, “Application of neuro - fuzzy system for predicting the success of a company in public procurement,” Decision Making: Applications in Management and Engineering, vol. 5, no. 1, pp. 135 –153, Apr. 2022.
  5. United Nations Development Programme. Human Development Report 2023/24: Breaking the Gridlock – Reimagining Cooperation in a Polarized World. New York: UNDP, 2024.
  6. Juned, V., Yusra, Y. Using Fuzzy C -Means Algorithm to Cluster Human Development Index // Communications in Mathematical Biology and Neuroscience.
  7. Annas, S., d.r. fuzzy c -means and gath -geva methods in clustering districts based on human development index (hdi) in south sulawesi. journal of physics: conference series, 2019.
  8. Chakravarty, S. R. The Multidimensional Measurement of Poverty: A Fuzzy Set Approach // in: Lemmi, A., Betti, G. (eds.). Fuzzy Set Approach to Multidimensional Poverty Measurement . New York: Springer, 2006. P. 57 –77.
  9. Alkire, S., Foster, J. Counting and Multidimensional Poverty Measurement // Journal of Public Economics. 2011. Vol. 95, no. 7 –8. P. 476 –487.
  10. Rakhimov, A. N. (2023). Factors influencing the prospective development of dehkan farms. Economics and Society, (3 -2 (106)), 255 -262
  11. Juraev, F. (2021). Perspective problems of agricultural production development and their econometric modeling. Economics and Education, (4), 377 -385.
  12. Maxmatkulov, G. O. X. (2023). A systematic approach to the development of the trade services sector. Educational Research in Universal Sciences, 2(10), 175 -182.
  13. Mirzayev, Sh. N. (2024). Applications of ADALINE in econometric modeling and financial decision -making. Financial Analysis Review, 5(3), 102 -118.

Downloads

Download data is not yet available.