Representation of a trend in OFN during fuzzy observance of the water level from the Crisis control center

This paper presents the issue of fuzzy arithmetic calculations in two different notations. The well-known L-R notation proposed by Dubois-Prade, which enjoys a well-earned recognition of the researchers dealing with fuzzy logic was presented on one hand. On the other hand, a OFN notation introduced by Kosiński was discussed. Comparative calculations were performed using the data of the benchmark “Dam and Crisis control center paradox”. That benchmark is available in two versions, where the opposite trend is visible at the dam and at the CCC (Crisis control center). In one of the versions, the water level at the observed area decreases and increases at the dam, while in the other version the situation is opposite. The trend difference detection can aid the short-term forecast of the situation change at the monitored area. Results of the applied calculations in OFN notation show that this arithmetic is sensitive to trend differences related to the order characteristic for those numbers. Relationship between the fuzzy logic and the trend of the observed phenomena is an added value to the generalization of OFN and it is also a good signal for the future development of applications of such fuzzy calculations, being their unique feature at the same time.

[1]  Siegfried Gottwald,et al.  Mathematical aspects of fuzzy sets and fuzzy logic: Some reflections after 40 years , 2005, Fuzzy Sets Syst..

[2]  Elbert A. Walker,et al.  The algebra of fuzzy truth values , 2005, Fuzzy Sets Syst..

[3]  Dominik Slezak,et al.  Algebraic Operations on Fuzzy Numbers , 2003, IIS.

[4]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  D. Dubois,et al.  Operations on fuzzy numbers , 1978 .

[6]  Giangiacomo Gerla Fuzzy Logic Programming and Fuzzy Control , 2005, Stud Logica.

[7]  Adam Grabowski,et al.  On the computer certification of fuzzy numbers , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[8]  Witold Kosinski,et al.  On Orientation Sensitive Defuzzification Functionals , 2014, ICAISC.

[9]  József Dombi,et al.  Towards a General Class of Operators for Fuzzy Systems , 2008, IEEE Transactions on Fuzzy Systems.

[10]  Dominik Ślęzak,et al.  Ordered fuzzy numbers , 2003 .

[11]  Iwona Skalna,et al.  Fuzzy multi-attribute evaluation of investments , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[12]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[13]  Didier Dubois,et al.  Gradual elements in a fuzzy set , 2008, Soft Comput..

[14]  Inés Couso,et al.  An Axiomatic Definition of Fuzzy Divergence Measures , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Didier Dubois Henri Prade FUZZY ELEMENTS IN A FUZZY SET , 2005 .

[16]  Witold Kosiński,et al.  On fuzzy number calculus , 2006 .

[17]  Krzysztof Pytel,et al.  A fuzzy logic approach to the evaluation of health risks associated with obesity , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[18]  Ivica Bosnjak,et al.  Algebras of fuzzy sets , 2009, Fuzzy Sets Syst..

[19]  Shichao Zhang,et al.  A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers , 2009, 2009 IEEE International Conference on Granular Computing.