Execution of arithmetic operations involving the second-order fuzzy numbers
DOI:
https://doi.org/10.15587/1729-4061.2020.210103Keywords:
fuzzy numbers of (L-R)-type, second-order fuzziness, algebra of operations, arithmetic operations, execution rulesAbstract
The need to improve the adequacy of conventional models of the source data uncertainty in order to undertake research using fuzzy mathematics methods has led to the development of natural improvement in the analytical description of the fuzzy numbers' membership functions. Given this, in particular, in order to describe the membership functions of the three-parametric fuzzy numbers of the (L-R)-type, the modification implies the following. It is accepted that these functions' parameters (a modal value, the left and right fuzzy factors) are not set clearly by their membership functions. The numbers obtained in this way are termed the second-order fuzzy numbers (bi-fuzzy). The issue, in this case, is that there are no rules for operating on such fuzzy numbers. This paper has proposed and substantiated a system of operating rules for a widely used and effective class of fuzzy numbers of the (L-R)-type whose membership functions' parameters are not clearly defined. These rules have been built as a result of the generalization of known rules for operating on regular fuzzy numbers. We have derived analytical ratios to compute the numerical values of the membership functions of the fuzzy results from executing arithmetic operations (addition, subtraction, multiplication, division) over the second-order fuzzy numbers. It is noted that the resulting system of rules is generalized for the case when the numbers-operands' fuzziness order exceeds the second order. The examples of operations execution over the second-order fuzzy numbers of the (L-R)-type have been given.
References
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8 (3), 338–353. doi: https://doi.org/10.1016/s0019-9958(65)90241-x
- Kaufman, A. (1975). Theory of Fuzzy subsets. Academic Press, 432.
- Dubois, D., Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9 (6), 613–626. doi: https://doi.org/10.1080/00207727808941724
- Babuska, R. (2003). Fuzzy Systems, Modeling and Identification. Prentice Hall.
- Lyu, B. (2005). Teoriya i praktika neopredelennogo programmirovaniya. Moscow: BINOM, 416.
- Remezova, E. M. (2013). Type-2 Fuzzy Sets: Conception, Analysis and Peculiarities Uzing. Modern problems of science and education, 5. Available at: http://www.science-education.ru/ru/article/view?id=10506
- Castillo, O., Melin, P. (2008). Type-2 Fuzzy Logic: Theory and Applications. Berlin: Springer-Verlag Berlin Heidelberg. doi: https://doi.org/10.1007/978-3-540-76284-3
- Hagras, H. A. (2004). A Hierarchical Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots. IEEE Transactions on Fuzzy Systems, 12 (4), 524–539. doi: https://doi.org/10.1109/tfuzz.2004.832538
- Celik, E., Gul, M., Aydin, N., Gumus, A. T., Guneri, A. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329–341. doi: https://doi.org/10.1016/j.knosys.2015.06.004
- Chang, P.-C., Wu, J.-L., Lin, J.-J. (2016). A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting. Applied Soft Computing, 38, 831–842. doi: https://doi.org/10.1016/j.asoc.2015.10.030
- Hamza, M. F., Yap, H. J., Choudhury, I. A., Chiroma, H., Kumbasar, T. (2017). A survey on advancement of hybrid type 2 fuzzy sliding mode control. Neural Computing and Applications, 30 (2), 331–353. doi: https://doi.org/10.1007/s00521-017-3144-z
- Tang, X., Deng, L., Yu, J., Qu, H. (2018). Output Feedback Predictive Control of Interval Type-2 T–S Fuzzy Systems With Markovian Packet Loss. IEEE Transactions on Fuzzy Systems, 26 (4), 2450–2459. doi: https://doi.org/10.1109/tfuzz.2017.2771502
- Xiao, B., Lam, H. K., Yang, X., Yu, Y., Ren, H. (2018). Tracking control design of interval type-2 polynomial-fuzzy-model-based systems with time-varying delay. Engineering Applications of Artificial Intelligence, 75, 76–87. doi: https://doi.org/10.1016/j.engappai.2018.08.002
- Zhang, Z., Niu, Y. (2018). Adaptive sliding mode control for interval type-2 stochastic fuzzy systems subject to actuator failures. International Journal of Systems Science, 49 (15), 3169–3181. doi: https://doi.org/10.1080/00207721.2018.1534027
- Mendel, J. M. (2019). Type-2 Fuzzy Sets as Well as Computing with Words. IEEE Computational Intelligence Magazine, 14 (1), 82–95. doi: https://doi.org/10.1109/mci.2018.2881646
- Du, Z., Yan, Z., Zhao, Z. (2019). Interval type-2 fuzzy tracking control for nonlinear systems via sampled-data controller. Fuzzy Sets and Systems, 356, 92–112. doi: https://doi.org/10.1016/j.fss.2018.02.013
- Shvedov, A. S. (2019). On Type-2 Fuzzy Sets and Type-2 Fuzzy Systems. Proceedings of the IV International Scientific Conference "Actual Problems of Applied Mathematics". Part I, Itogi Nauki i Tekhniki. Ser. Sovrem. Mat. Pril. Temat. Obz., 165, 114–122. Available at: http://www.mathnet.ru/links/0acb6b17421b39ba90bad684804911ac/into471.pdf
- Raskin, L., Sira, O. (2020). Performing arithmetic operations over the (L–R)-type fuzzy numbers. Eastern-European Journal of Enterprise Technologies, 3 (4 (105)), 6–11. doi: https://doi.org/10.15587/1729-4061.2020.203590
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Lev Raskin, Oksana Sira
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.