journal article·2021-06-23Open access

Multidimensional Type 2 Epistemic Fuzzy Arithmetic Based on the Body Definition of the Type 2 Fuzzy Set

Andrzej Piegat, Marek Landowski

Applied Sciences· Volume 11· Issue 13

Abstract

The article presents a multidimensional type 2 epistemic fuzzy arithmetic (MT2EF-arithmetic) based on the new body definition of fuzzy set type 2 (T2FS), which in the authors’ opinion, is more suitable for fuzzy computing than the current versions of fuzzy arithmetic (FA) based on the border definition of T2FS. The proposed MT2EF-arithmetic is designed for epistemic variables and has mathematical properties that allow for obtaining universal algebraic calculation results. MT2EF-arithmetic performs calculations, not only with borders of fuzzy numbers, but also with whole bodies of FNs. Thanks to this, computational tasks are solved in the full space of the problem and not in a limited, low-dimensional space. As a result, MT2EF-arithmetic provides precise solutions to problems, solutions that are neither overestimated, underestimated, nor shifted. The paper contains an example of MT2EF-application to optimal fertilization of beetroot cultivation with nitrogen.
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Research context

OpenAlex topics: Fuzzy Logic and Control Systems · Multi-Criteria Decision Making · Fuzzy Systems and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Computer Science, West Pomeranian University of Technology, 71-210 Szczecin, Poland

Faculty of Computer Science and Telecommunications, Maritime University of Szczecin, 70-500 Szczecin, Poland

Funding

Ministry of Education and Science

References (49)

Show source-provided references
1. A critical review on theoretical drawbacks and mathematical incorrect assumptions in fuzzy OR methods: Review from 2010 to 202010.1016/j.asoc.2020.106354 ↗
2. The concept of a linguistic variable and its application to approximate reasoning-I10.1016/0020-0255(75)90036-5 ↗
3. Hanss, M. (2005). Applied Fuzzy Arithmetic, Springer.
4. Klir, G.J., and Cooper, J.A. (1996, January 11). On constrained fuzzy arithmetic. Proceedings of the IEEE 5th International Fuzzy Systems, New Orleans, LA, USA.
5. Operations on fuzzy numbers10.1080/00207727808941724 ↗
6. Dubois, D., and Prade, H. (1988). Possibility Theory, Springer.
7. Gradual elements in a fuzzy sets10.1007/s00500-007-0187-6 ↗
8. Gradual interval arithmetic and fuzzy interval arithmetic10.1007/s41066-019-00208-z ↗
9. Interval linear systems as a necessary step in fuzzy linear systems10.1016/j.fss.2015.03.018 ↗
10. Atanassov, K.T. (2014). Correctness checking of uncertain-equation solutions on example of interval-modal-method. Modern Approach in Fuzzy Sets, System Research Institute of Polish Academy of Sciences, IBS PAN.
11. Fuzzy number addition with application of horizontal membership functions10.1155/2015/367214 ↗
12. Horizontal membership function and examples of its applications10.1007/s40815-015-0013-8 ↗
13. Fuzzy number division and the multigranularity phenomenon
14. Fuzzy arithmetic type 1 with horizontal membership functions
15. Is an interval the right result of arithmetic operations on intervals?10.1515/amcs-2017-0041 ↗
16. Is the conventional interval-arithmetic correct?
17. Two interpretations of multidimensional rdm interval arithmetic-multiplication and division
18. Kolodziejczyk, J., Piegat, A., and Salabun, W. (2020). Which alternative for solving dual fuzzy nonlinear equations is more precise?. Mathematics, 8.10.3390/math8091507 ↗
19. Arithmetic operators in interval-valued fuzzy set theory
20. Linear programming problem with interval type 2 fuzzy coefficients and an interpretation for constraints10.1155/2016/8496812 ↗
21. Fuzzy optimization techniques by hidden Markov model with interval type-2 fuzzy parameters10.1007/s40815-019-00738-3 ↗
22. Encoding words into normal interval type-2 fuzzy sets: Hm approach10.1109/TFUZZ.2015.2486814 ↗
23. A PROMETHEE-based outranking method for multiple criteria decision analysis with interval type-2 fuzzy sets10.1007/s00500-013-1109-4 ↗
24. Hamrawi, H., and Coupland, S. (2009, January 20–24). Type-2 fuzzy arithmetic using alpha-planes. Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal.
25. Coupland, S., and John, R. (2003, January 1–3). An approach to type-2 fuzzy arithmetic. Proceedings of the UK Workshop on Computational Intelligence, Bristol, UK.
26. Type-2 fuzzy variables and their arithmetic10.1007/s00500-009-0461-x ↗
28. Interval type-2 capital budgeting10.1007/s40815-015-0040-5 ↗
29. Multi-criteria decision-making method based on type-2 fuzzy sets10.2298/FIL1702431W ↗
30. Type-2 FLCs: Interactive fuzzy goal programming based on Taylor series to solve multiobjective nonlinear programming problems with interval type 2 fuzzy numbers10.1109/TFUZZ.2017.2774191 ↗
31. Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets
32. Shortest path problem in network with type-2 triangular fuzzy arc length
33. An analytical solution to the TOPSIS model with interval type-2 fuzzy sets10.1007/s00500-014-1584-2 ↗
34. Turk, S., John, R., and Ozcan, E. (2014, January 8–10). Interval type-2 fuzzy sets in supplier selection. Proceedings of the 2014 14th UK Workshop on Computational Intelligence (UKCI), Bradford, UK.10.1109/UKCI.2014.6930168 ↗
35. Jaffal, H., and Tao, C. (2011). Multiple-Attributes Group Decision-Making by Type-2 Fuzzy Sets and Systems. [Master’s Thesis, Blekinge Institute of Technology, Department of Mathematics and Sciences].
36. Type-2 FLCs: A new generation of fuzzy controllers10.1109/MCI.2007.357192 ↗
37. Type-2 triangular fuzzy matrices in medical diagnosis
38. Interval type-2 fuzzy modelling and stochastic search for real-world inventory management10.1007/s00500-012-0848-y ↗
39. Type-2 fuzzy linear systems10.1007/s41066-016-0037-y ↗
40. Piegat, A., and Landowski, M. (2019, January 24–25). In direction of intuitionistic fuzzy arithmetic. Proceedings of the Eighteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Warsaw, Poland.
41. Schmidt, G. (2010). Relational Mathematics, Cambridge University Press. [1st ed.].10.1017/CBO9780511778810 ↗
42. Method with horizontal fuzzy numbers for solving real fuzzy linear systems10.1007/s00500-018-3290-y ↗
43. Mendel, J.M. (2017). Uncertain Rule Based Fuzzy Systems, Introduction and New Directions, Springer International Publishing. [2nd ed.].10.1007/978-3-319-51370-6 ↗
44. On clarifying some definitions and notations used for type-2 fuzzy sets as well as some recommended changes10.1016/j.ins.2016.01.015 ↗
45. From interval-valued data to general type-2 fuzzy sets10.1109/TFUZZ.2014.2310734 ↗
46. A new method for parameterization of general type-2 fuzzy sets10.1080/16168658.2018.1509519 ↗
47. Yakov, B.H. (2006). Info-Gap Decision Theory, Academic Press. [2nd ed.].
48. The effects of nitrogen, potassium and sodium fertilizers on sugar beef10.1017/S0021859600083933 ↗
49. Stanek, R. (2012). Good agricultural practices in integrated production with the use of beneficial microorganisms in beet cultivation. Dobre Praktyki Rolnicze w Produkcji Zintegrowanej z Zastosowaniem Pozytecznych Mikroorganizmow w Uprawie Buraka, Ministerstwo Rolnictwa i Rozwoju Wsi, Fundacja Programow Pomocy dla Rolnictwa FAPA.