Z-SONLАRDA NOАNIQLIK VА ISHONCHLILIKNI BIRGАLIKDА IFODАLАSHNING ZАMONАVIY YONDАSHUVI

Dilnoz Muhamediyeva; Dilfuza Yusupova; Dilmurod Yusupov; Shohruhbek Yo‘ldoshev

Authors

Dilnoz Muhamediyeva "Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti" milliy tadqiqotlar universiteti
Dilfuza Yusupova Ipak yo‘li innovatsiyalar universiteti , Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti
Dilmurod Yusupov “CYBERUNIVERSIT” davlat universiteti
Shohruhbek Yo‘ldoshev Chirchiq davlat pedagogika universiteti

Keywords:

Z- sonlar, noravshan sonlar, ishonch darajasi, noravshanlik, tegishlilik funksiyasi, xavf-xatar tahlili

Abstract

Ushbu maqolada Lotfi Zade tomonidan taklif etilgan Z-sonlar konsepsiyasi haqida so‘z boradi. Z-sonlar ikki qismdan - qiymatning noravshan tavsifi va unga bo‘lgan ishonch darajasidan - iborat bo‘lib, kundalik hayotda uchraydigan taxminiy va subyektiv ma’lumotlarni hisobga olish imkonini beradi. Maqolada Z-sonlarning matematik tavsifi, turlari va ularni tavsiflash usullari yoritiladi. Shuningdek, ushbu yondashuvning iqtisodiyot, xavf-xatar tahlili va boshqaruv sohalaridagi amaliy qo‘llanish imkoniyatlari, afzalliklari hamda mavjud muammolari tahlil qilinadi.

References

1. MUHAMMAD A., INAYAT U. Solution of Z-number-based multi-objective linear programming models with different membership functions // Information Sciences. – 2024. – Vol. 659. – P. 120100.

2. ZADEH L.A. A note on Z-numbers // Information Sciences. – 2011. – Vol. 181. – P. 2923–2932.

3. ANJARIA K. Knowledge derivation from Likert scale using Z-numbers // Information Sciences. – 2022. – Vol. 590. – P. 234–252.

4. JISKANI I.M., YASLI F., HOSSEINI S., REHMAN A.U., UDDIN S. Improved Z-number based fuzzy fault tree approach to analyze health and safety risks in surface mines // Resources Policy. – 2022. – Vol. 76. – P. 102591.

5. GHAHTARANI A. A new portfolio selection problem in bubble condition under uncertainty: Application of Z-number theory and fuzzy neural network // Expert Systems with Applications. – 2021. – Vol. 177. – P. 114944.

6. KANG B., WEI D., LI Y., DENG Y. A method of converting Z-number to classical fuzzy number // Journal of Information and Computational Science. – 2012. – Vol. 9. – P. 703–709.

7. ALIEV R.A., ALIZADEH A.V., HUSEYNOV O.H. The arithmetic of discrete Z-numbers // Information Sciences. – 2015. – Vol. 290. – P. 134–155.

8. ALIEV R.A., HUSEYNOV O.H., ZEINALOVA L.M. The arithmetic of continuous Z-numbers // Information Sciences. – 2016. – Vol. 373. – P. 441–460.

9. ALIEV R.A., PEDRYCZ W., HUSEYNOV O.H. Functions defined on a set of Z-numbers // Information Sciences. – 2018. – Vol. 423. – P. 353–375.

10. ALIZADEH A.V., ALIYEV R.R., HUSEYNOV O.H. Algebraic properties of Z-numbers under additive arithmetic operations // Proceedings of the 13th International Conference on Application of Fuzzy Systems and Soft Computing. – Warsaw, Poland, 27–28 August 2018. – Vol. 896. – P. 893–900.

11. PENG H.G., ZHANG H.Y., WANG J.Q., LI L. An uncertain Z-number multicriteria group decision-making method with cloud models // Information Sciences. – 2019. – Vol. 501. – P. 136–154.

12. CHENG R.L., KANG B.Y., ZHANG J.F. An improved method of converting Z-number into classical fuzzy number // Proceedings of the 33rd Chinese Control and Decision Conference. – Kunming, China, 22–24 May 2021. – P. 3823–3828.

13. MAZANDARAM M., ZHAO Y. Z-differential equation // IEEE Transactions on Fuzzy Systems. – 2020. – Vol. 28. – P. 462–473.

14. WU H., YUE Q., GUO P., PAN Q., GUO S.S. Sustainable regional water allocation under water-energy nexus: A chance-constrained possibilistic mean-variance multi-objective programming // Journal of Cleaner Production. – 2021. – Vol. 313. – P. 127934.

15. ZADEH L.A. Fuzzy sets as a basis for a theory of possibility // Fuzzy Sets and Systems. – 1978. – Vol. 1. – P. 3–28.

16. ZHAO M., HAN Y., ZHOU J. An extensive operational law for monotone functions of LR fuzzy intervals with applications to fuzzy optimization // Soft Computing. – 2022. – Vol. 26. – P. 11381–11401.

17. GU Y., HAO Q., SHEN J., ZHANG X., YU L. Calculation formulas and correlation inequalities for variance bounds and semivariances of fuzzy intervals // Journal of Intelligent and Fuzzy Systems. – 2019. – Vol. 36. – P. 353–369.

18. GENTILI P.L. Establishing a new link between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability: Perspectives in artificial intelligence and unconventional computing // Molecules. – 2021. – Vol. 26. – P. 5987.