logo
calendar27 Noyabr 2019
view40
Main language:Russian

Approaches for use z-evaluation of uncertainty systems fuzzy conclusion

Field of Science:
pdf

5dde43922a795.pdf

PDF

ARTICLE ANNOTATION

quote
this paper the construction of a model based on fuzzy inference rules using the Z-evaluation aimed at making inferences based on the use of vague, inaccurate or incomplete initial information. Proposed а general approach for the calculation under Z-numbers on the basis of extension of Zadeh. It sets out the basic arithmetic discrete Z-numbers. Considered three types fuzzy assessment model for weakly formalized process, whose output is a linear and non-linear dependence, as well as fuzzy terms. Computational experiments and made an analysis of the results.

AUTHORS

D.Muxamediyeva

Toshkent axborot texnologiyalari universiteti

X.Primova

Toshkent axborot texnologiyalari universiteti

N.Niyozmatova

Toshkent axborot texnologiyalari universiteti

Tags

# дискретное Z-число# Z-оценивание# нечеткое правило# нечеткий вывод# discrete Z-number# Z-evaluation# fuzzy rules# fuzzy inference# diskrеt Z-soni# Z-baholash# noravshan qoidalar# noravshan xulosa

OTHER ARTICLES IN THIS JOURNAL

Rate Article

0
0 ratings
5
4
3
2
1

Article Identifiers

References

Zadeh L.A. A note on a Z-number, Information Sciences 181. – USA, (2011). – Рp. 2923-2932.

Kang B., Wei D., Li Y., Deng Y. A Method of Converting Z-number to Classical Fuzzy Number, Journal of Information & Computational Science 9(3), USA, (2012). – Рp. 703-709.

Zadeh L.A. Fuzzy sets, Information and Control 8, USA, (1965). – Рp. 338-353.

Kaufmann A., Gupta M. Introduction to fuzzy arithmetic: Theory and application, Van Nostrand Reinhold Co. – New York, 1985.

Klir G., Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, New Jersey, 1995.

Kang B., Wei D., Li Y., Deng Y. Decision Making Using Z-numbers under Uncertain Environment, Journal of Information & Computational Science 8(7) , USA, (2012). – Рp. 2807-2814.

Yager R.R. On a View of Zadeh's Z-Numbers, Advances in Computational Intelligence, Communications in Computer and Information Science 299, USA, (2012). – Рp. 90-101.

Yager R.R. On Z-valuations using Zadeh`s Z-numbers, International Journal of Intelligent Systems 27, (2012). – Рp. 259-278.

Sevastianov P. Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster–Shafer theory, Information Sciences 177 (21) (2007). – Рp.4645-4661.

Zhai D., Mendel J. Uncertainty measures for general type-2 fuzzy sets, Information Sciences 181(3), USA, (2011). – Рp. 503-518.

Zadeh L.A. Methods and systems for applications with Z-numbers, United States Patent, Patent No.: US 8,311,973 B1. – Date of Patent: Nov. 13, 2012.

Aliev R.A., Zeinalova L.M. Decision making under Z-information, In: P. Guo, W. Pedrycz (Eds.), Human Centric Decision-Making Models for Social Sciences (Studies in Computational Intelligence), Springer, Berlin Heidelberg, 2013.

Aliev R.A., Pedrycz W., Fazlollahi B., Huseynov O.H., Alizadeh A.V., Guirimov B.G. Fuzzy logic-based generalized decision theory with imperfect information, Information Sciences 189 (2011). – Рp.18-42.

Farina M., Amato P. A fuzzy definition of "optimality" for many-criteria optimization problems, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34(3) (2004). – Рp. 315-326.

Aliev R.A. Fundamentals of the Fuzzy Logic-Based Generalized Theory of Decisions, Studies in Fuzziness and Soft Computing, 293, Springer, Berlin Heidelberg, 2013.