Development of the method of general interpolation for Z-number-valued if-then rules

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.286164

Keywords:

Z-number, fuzzy number, partial reliability, if-then rules, interpolation, distance, weights

Abstract

Rule interpolation-based methods are used when the rule base is sparse. This frequently being the case, as information relevant to real-world problems is not usually comprehensive. At the same time, relevant information is often characterized by both fuzziness and partial reliability. To deal with such kind of information, the concept of Z-number was introduced by Zadeh. This paper is devoted to an extension of the general interpolation method for fuzzy rules to the case of if-then rules with Z-number-valued antecedents and consequents. The proposed approach relies on the determination of the distance between the current observation vector and vectors of rules antecedents. By determining the distance between the current vector and the antecedents of the rules, decisions can be made based on the nearest antecedents. In this context, rule antecedents are vectors that represent certain conditions. The resulting output is computed as a weighted sum of rules consequents. Weighting factors are used to account for the importance of each rule in the interpolation. Weights of interpolations are found on the basis of mentioned distance values. The results of this study are aimed at developing an approach to decision-making in terms of Z-valued information. The method is characterized by relatively low computational complexith. Regarding the application of the proposed approach, the job satisfaction evaluation problem is considered. Consequently, the obtained results confirm the efficiency of the proposed approach. The proposed method can be a useful tool for decision-making in various applications, especially where high computational complexity is unacceptable or impractical

Author Biographies

Konul Jabbarova, Azerbaijan State Oil and Industry University

PhD on Technical Sciences, Associated Professor

Department of Computer Engineering

Ulviyya Rzayeva, Azerbaijan State University of Economics (UNEC)

PhD on Mathematics, Associated Professor

Department of Digital Technologies and Applied Informatics

Aynur Jabbarova, Azerbaijan State University of Economics (UNEC)

Candidate of Economic Sciences, Associated Professor

Department of Mathematics and Statistics

References

  1. Zadeh, L. A. (2011). A Note on Z-numbers. Information Sciences, 181 (14), 2923–2932. doi: https://doi.org/10.1016/j.ins.2011.02.022
  2. Chen, S.-M., Chang, Y.-C. (2011). Fuzzy rule interpolation based on interval type-2 Gaussian fuzzy sets and genetic algorithms. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). doi: https://doi.org/10.1109/fuzzy.2011.6007533
  3. Huang, Z. (2006). Rule Model Simplification. University of Edinburgh. Available at: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=fb72b35e95303843a5c4f661ca162c65678a4a65
  4. Li, F., Shang, C., Li, Y., Yang, J., Shen, Q. (2021). Approximate reasoning with fuzzy rule interpolation: background and recent advances. Artificial Intelligence Review, 54 (6), 4543–4590. doi: https://doi.org/10.1007/s10462-021-10005-3
  5. Alzubi, M., Johanyák, Z. C., Kovács, Sz. (2018). Fuzzy Rule Interpolation Methods and Fri Toolbox. Journal of Theoretical and Applied Information Technology, 96 (21). Available at: https://www.researchgate.net/publication/329239835_FUZZY_RULE_INTERPOLATION_METHODS_AND_FRI_TOOLBOX
  6. Naik, N., Diao, R Shen, Q. (2018). Dynamic Fuzzy Rule Interpolation and Its Application to Intrusion Detection. IEEE Transactions on Fuzzy Systems, 26 (4), 1878–1892. doi: https://doi.org/10.1109/tfuzz.2017.2755000
  7. Das, S., Chakraborty, D., Kóczy, L. T. (2019). Linear fuzzy rule base interpolation using fuzzy geometry. International Journal of Approximate Reasoning, 112, 105–118. doi: https://doi.org/10.1016/j.ijar.2019.05.004
  8. Tikk, D., Johanyák, Z. C., Kovács, S., Wong, K. W. (2011). Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15 (3), 254–263. doi: https://doi.org/10.20965/jaciii.2011.p0254
  9. Chen, C., Parthaláin, N. M., Li, Y., Price, C., Quek, C., Shen, Q. (2016). Rough-fuzzy rule interpolation. Information Sciences, 351, 1–17. doi: https://doi.org/10.1016/j.ins.2016.02.036
  10. Chen, S.-M., Lee, L.-W. (2011). Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. Expert Systems with Applications, 38 (8), 9947–9957. doi: https://doi.org/10.1016/j.eswa.2011.02.035
  11. Aliev, R. A., Pedrycz, W., Huseynov, O. H., Eyupoglu, S. Z. (2017). Approximate Reasoning on a Basis of Z-number valued If-Then Rules. IEEE Transactions on Fuzzy Systems, 25 (6), 1589–1600. doi: https://doi.org/10.1109/tfuzz.2016.2612303
  12. Aliev, R. A., Huseynov, O. H., Zulfugarova, R. X. (2016). Z-Distance Based IF-THEN Rules. The Scientific World Journal, 2016, 1–9. doi: https://doi.org/10.1155/2016/1673537
  13. Aliev, R. A., Alizadeh, A. V., Huseynov, O. H. (2015). The arithmetic of discrete Z-numbers. Information Sciences, 290, 134–155. doi: https://doi.org/10.1016/j.ins.2014.08.024
  14. Aliev, R. A., Guirimov, B. G., Huseynov, O. H., Aliyev, R. R. (2021). Z-relation equation-based decision making. Expert Systems with Applications, 184, 115387. doi: https://doi.org/10.1016/j.eswa.2021.115387
  15. Alonso de la Fuente, M., Terán, P. (2023). Convergence in distribution of fuzzy random variables in L-type metrics. Fuzzy Sets and Systems, 470, 108653. doi: https://doi.org/10.1016/j.fss.2023.108653
  16. Abiyev, R. H., Saner, T., Eyupoglu, S., Sadikoglu, G. (2016). Measurement of Job Satisfaction Using Fuzzy Sets. Procedia Computer Science, 102, 294–301. doi: https://doi.org/10.1016/j.procs.2016.09.404
  17. Lepot, M., Aubin, J.-B., Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9 (10), 796. doi: https://doi.org/10.3390/w9100796
  18. Alam, N. M. F. H. N. B., Ku Khalif, K. M. N., Jaini, N. I., Gegov, A. (2023). The Application of Z-Numbers in Fuzzy Decision Making: The State of the Art. Information, 14 (7), 400. doi: https://doi.org/10.3390/info14070400
Development of the method of general interpolation for Z-number-valued if-then rules

Downloads

Published

2023-08-31

How to Cite

Jabbarova, K., Rzayeva, U., & Jabbarova, A. (2023). Development of the method of general interpolation for Z-number-valued if-then rules. Eastern-European Journal of Enterprise Technologies, 4(4 (124), 19–26. https://doi.org/10.15587/1729-4061.2023.286164

Issue

Section

Mathematics and Cybernetics - applied aspects