Comparative Analysis of Fuzzy Set Defuzzification Methods in the Context of Ecological Risk Assessment

Oļegs Užga-Rebrovs, Gaļina Kuļešova

Abstract


Fuzzy inference systems are widely used in various areas of human activity. Their most widespread use lies in the field of fuzzy control of technical devices of different kind. Another direction of using fuzzy inference systems is modelling and assessment of different kind of risks under insufficient or missing objective initial data. Fuzzy inference is concluded by the procedure of defuzzification of the resulting fuzzy sets. A large number of techniques for implementing the defuzzification procedure are available nowadays. The paper presents a comparative analysis of some widespread methods of fuzzy set defuzzification, and proposes the most appropriate methods in the context of ecological risk assessment.


Keywords:

Defuzzification methods; ecological risk; ecological risk assessment; fuzzification; fuzzy inference; fuzzy inference system

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References


E. H. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, p. 1585, 1974. https://doi.org/10.1049/piee.1974.0328

A. F Shapiro and M. -S. Koissi. Risk Assessment Application of Fuzzy Logic. Society of Actuaries Canadian Institute of Actuaries, 2015.

L. A. Zadeh, “Fuzzy logic = computing with words,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 2, pp. 103–111, May 1996. https://doi.org/10.1109/91.493904

T. J. Ross, Fuzzy Logic with Engineering Applications, 2nd ed. John Wiley and Sons Ltd, 2004.

R. R. Yager and L. A. Zadeh Ed., An Introduction to Fuzzy Logic Applications in Intelligent Systems. Springer Science; Business Media, LLC, 1992.

C. León, “Operational Risk Management Using a Fuzzy Logic Inference System,” SSRN Electronic Journal, 2009. https://doi.org/10.2139/ssrn.1473614

Ridong Du, Yongbo Yuan, and Miao Chen, “Construction Vibration Risk Assessment Based on Fuzzy Logic,” International Journal on Advances in Information Sciences and Service Sciences, vol. 5, no. 4, pp. 664–675, Feb. 2013. https://doi.org/10.4156/aiss.vol5.issue4.81

S. S. M. Khalifa, K. Saadan, and N. Md Norwawi, “Risk Assessment of Mined Areas using Fuzzy Inference,” International Journal of Artificial Intelligence & Applications, vol. 6, no. 2, pp. 37–51, Mar. 2015. https://doi.org/10.5121/ijaia.2015.6203

A. Radionovs and O. Uzhga-Rebrov, “Environmental Risk Assessment by Fuzzy Multiple Criteria Decision Making Approach”, in International Scientific School “Modelling and Analysis of Safety and Risk in Complex Systems”, Saint-Petersburg, Russia, pp. 119–124. Oct. 25–28, 2016.

T. A. Runkler, “Extended defuzzification methods and their properties,” Proceedings of IEEE 5th International Fuzzy Systems, pp. 694–700, 1996. https://doi.org/10.1109/fuzzy.1996.551822

A. Amini and N. Nikraz, “Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights,” International Journal of Intelligent Systems and Applications, vol. 8, no. 2, pp. 1–12, Feb. 2016. https://doi.org/10.5815/ijisa.2016.02.01

W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy Sets and Systems, vol. 108, no. 2, pp. 159–178, Dec. 1999. https://doi.org/10.1016/s0165-0114(97)00337-0

T. A. Runkler and M. Glesner, “A set of axioms for defuzzification strategies towards a theory of rational defuzzification operators,” Second IEEE International Conference on Fuzzy Systems, pp. 1161–1166, 1993. https://doi.org/10.1109/fuzzy.1993.327350

S. Naaz, A. Alam and R. Biswas, “Effect of different defuzzification methods in a fuzzy based load balancing application,” International Journal of Computer Science Issues, vol. 8, issue 5, pp. 261–267, 2011.

K. K. Uraon and S. Kumar, “Analysis of Defuzzification Method for Rainfall Event,” International Journal of Computer Science and Mobile Computing, vol. 1, issue 1, pp. 341–354, 2016.

D.Wilczyńska–Sztyma and K.Wielki, “Direction of Research into Methods of Defuzzification for Ordered Fuzzy Numbers”, presented at XII International PhD Workshop, 23–26 October, 2010, pp. 105–110.


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