Minkowski’s Inequality Based Sensitivity Analysis of Fuzzy Signatures

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Abstract

Fuzzy signatures were introduced as special tools to describe and handle complex systems without their detailed mathematical models. The input parameters of these systems naturally have uncertainties, due to human activities or lack of precise data. These uncertainties influence the final conclusion or decision about the system. In this paper we discuss the sensitivity of the weigthed general mean aggregation operator to the uncertainty of the input values, then we analyse the sensitivity of fuzzy signatures equipped with these aggregation operators. Finally, we apply our results to a fuzzy signature used in civil enginnering.

[1] K. W. Wong, T. D. Gedeon, L. T. Kóczy, Construction of fuzzy signature from data: an example of SARS pre-clinical diagnosis system, in: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2004), Budapest, Hungary, 2004, pp.1649-1654.

[2] Á. Ballagi, L. T. Kóczy, T. D. Gedeon, Robot cooperation without explicit communication by fuzzy signatures and decision trees, in: Proceedings of theJoint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT2009), Lisbon, Portugal, 2009, pp.1468-1473.

[3] T. Vámos, L. T. Kóczy , G. Biró, Fuzzy signatures in datamining, in: Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, 2001, pp. 2842-2846 (5).

[4] G. Molnárka, L. T. Kóczy, Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures, Int. J. of Computers , Communications & Control, 2011, No. 3, pp. 442-457.

[5] C. Pozna, N. Minculete, R. E. Precup, L. T. Kóczy, Á. Ballagi, Signatures: Definitions, operators and applications to fuzzy modelling, Fuzzy Sets and Systems 201 (2012), pp. 86-104.

[6] L. T. Kóczy, T. Vámos, G. Biró, Fuzzy signatures, in: Proceedings of the 4th Meeting of the Euro Working Group on Fuzzy Sets and the 2nd International Conference on Soft and Intelligent Computing (EUROPUSE-SIC99), Budapest, Hungary, 1999, pp. 210-217.

[7] J.A.Goguen, L-fuzzy sets, J. Math. Anal. Appl. 18(1) (1967), pp. 145-174.

[8] I. Á. Harmati, Á. Bukovics, L. T. Kóczy, Sensitivity Analysis of the Weighted Generalized Mean Aggregation Operator and its Application to Fuzzy Signatures, IEEE World Congress on Computational Intelligence (WCCI 2014 - FUZZ-IEEE 2014). Peking, Kna, 2014.07.06-2014.07.11. New York: IEEE, 2014. pp. 1327-1332.

[9] U. Kaymak, H.R. van Nauta Lemke, T. Boer: A sensitivity-based analysis of weighted fuzzy aggregation, In: Proceedings of the IEEE World Congress on Computational Intelligence, IEEE International Conference on Fuzzy Systems, IEEE, 1998. pp. 755-760.

[10] V. Torra: Sensitivity analysis for WOWA, OWA and WM operators, In: Proceedings of ISIE 2001, IEEE International Symposium on Industrial Electronics, IEEE, 2001. pp. 134-137.

[11] M. Zarghami, F. Szidarovszky: Fuzzy quantifiers in sensitivity analysis of OWA operator, Computers & Industrial Engineering 54 (2008), pp. 1006-1018.

[12] G. H. Hardy, J. E. Littlewood, G. Pólya: Inequalities, Cambridge University Press, 1952.

[13] P. S. Bullen: Handbook of means and their inequalities, Kluwer Academic Publishers, 2003.

[14] G. H. Golub, C. F. van Loane: Matrix computations, John Hopkins University Press, 1996.

[15] Á. Bukovics, L.T. Kóczy, Fuzzy Signature-based Model for Qualification and Ranking of Residential Buildings, XXXVIII. IAHSWorld Congress on Housing, Istanbul, Turkey, 2012. pp. 290-297

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

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