Document Type : Research Paper
Authors
1 ASOIU
2 Department of Electrical & Computer Engineering, University of Alberta, Edmonton
3 Department of Computer-Aided Control Systems, Azerbaijan State Oil Academy, 20 Azadlig Ave., AZ1010 Baku, Azerbaijan
4 Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics, Azerbaijan State Oil and Industry University, 20 Azadlig Ave., AZ1010, Baku, Azerbaijan
5 DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL ACADEMY, BAKU, AZERBAIJAN
Abstract
The concept of Z-number was introduced by Zadeh in order to deal with partial reliability of information. This concept
describes a fusion of fuzzy and probabilistic types of uncertainty. In turn, one of the main fields of dealing with imperfect
information is approximate reasoning. For the case of pure fuzzy information this field is well-developed. In contrast,
existing studies on reasoning with Z-valued “if-then” rules are scarce. One of the main reasons is high analytical and
computational complexity. In this work, we develop an approach to reasoning with such kind of rules. The original
approach proposed here allows to deal with sparse rule base and is characterized by relatively low computational
complexity. The new concept of similarity of Z-numbers based on Jaccard similarity index and measure of divergence
of probability distributions is introduced. Based on similarity degrees of current input Z-numbers and Z-numbers
located in rule antecedents, weights of linear combination of Z-numbers in rule consequents are determined. The linear
combination is based on operations with Z-numbers proposed by authors. Applications of the proposed approach to
evaluation of economic development level for a country and control problem are considered.
Keywords
Main Subjects
Onar, B. Oztaysi, I. Sari, S. Cebi, A. Tolga, (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions.
Advances in Intelligent Systems and Computing, 1197, 964-72. DOI:10.1007/978-3-030-51156-2 112.
Procedia Computer Science, 120 (2017), 608-616. DOI:10.1016/j.procs.2017.11.286.
manufacturing, Verlag, 1991.
(2015), 134-155. DOI: 10.1016/j.ins.2014.08.024.
(2016), 441-460. DOI: 10.1016/j.ins.2016.08.078.
described by matrices of Z-numbers, International Journal of Information Technology and Decision Making, 19(06)
(2020), 1429-1450. DOI:10.1142/s0219622020500340.
\If-Then" rules, IEEE Transactions on Fuzzy Systems, 25(6) (2017), 1589-600. DOI:10.1109/tfuzz.2016.2612303.
Sciences, 181(6) (2011), 1045-1059. DOI:10.1016/j.ins.2010.11.021.
2800-2812. DOI:10.1109/tfuzz.2021.3094657.
nal fuzzy sets and novel move and transformation techniques, Information Sciences, 489 (2019), 303-315.
DOI:10.1016/j.ins.2019.03.034.
spherical fuzzy distance and similarity measures and their applications to medical diagnosis, Expert Systems with
Applications, 191 (2022). DOI:10.1016/j.eswa.2021.116330.
Reasoning, 137 (2021), 166-180. DOI:10.1016/j.ijar.2021.07.009.
inference: Prediction of welding distortion, Journal of Zhejiang University-Science C, 15 (2014), 636-650.
DOI:10.1631/jzus.c1300370.
relational factorization, Expert Systems with Application, (2022). DOI: 10.1016/j.eswa.2022.117904.
belief functions framework, Information Sciences, 309 (2015), 26-49. DOI: 10.1016/j.ins.2015.03.005.
19(4) (2022), 107-124. DOI: 10.22111/ijfs.2022.7090.
zero-order TSK fuzzy inference system, IEEE Transactions on Fuzzy Systems, 30(5) (2022), 1248-1259.
DOI:10.1109/tfuzz.2021.3057239.
124-139. DOI:10.1016/j.fss.2019.07.013.
Journal of Approximate Reasoning, 9(3) (1993), 197-225. DOI:10.1016/0888-613x(93)90010-b.
79-86. DOI: 10.1214/aoms/1177729694.
A visual interpretable framework, Applied Soft Computing, 134 (2023). DOI:10.1016/j.asoc.2022.109958.
fuzzy sets, Iranian Journal of Fuzzy Systems, 15(5) (2018), 95-116. DOI: 10.22111/ijfs.2018.4161.
and Algebraic Methods in Programming, 113 (2020). DOI:10.1016/j.jlamp.2020.100541.
inference and interpolation, Knowledge-Based Systems, 258 (2022). DOI:10.1016/j.knosys.2022.109995.
Conference on Fuzzy Systems (Cat No01CH37297), (2001). DOI:10.1109/fuzz.2001.1008853.
485. DOI:10.3390/e21050485.
Applications, 2(1) (2014). DOI: 10.1186/s40467-014-0018-0.
CRC Press, 2017.
making, Studies in Systems, Decision and Control, (2022), 3-25. DOI: 10.1007/978-3-030-99776-2-1.
Press; 2007. DOI:10.1002/9780470168967.
6(1) (2008), 47-71. DOI: 10.1016/j.jal.2007.01.001.
3 (2012), 293-310. DOI: 10.2298/PAN1203293S.
Systems, 10(5) (2013), 1-24. DOI: 10.22111/ijfs.2013.1204.
Fuzzy Sets and Systems, 298 (2016), 207-221. DOI:10.1016/j.fss.2015.11.002.
represented as fuzzy belief functions, Neurocomputing, 166 (2015), 319-336. DOI: 10.1016/j.neucom.2015.03.068.
DOI: 10.1109/tfuzz.2011.2161584.
Conference on Fuzzy Systems, (1992). DOI: 10.1109/fuzzy.1992.258757.
Fuzzy Systems, 30(10) (2022), 4031-4043. DOI:10.1109/tfuzz.2021.3136359.
0.