The extension principle and a decomposition of fuzzy sets

Information Sciences

Volumn 177, Issue 23, 2007, Pages 5394-5403

  Chalco Cano, Y ^a   Roman Flores H ^a   Rojas Medar, M ^b   Saavedra, O R ^c   Jimenez Gamero M D ^d  

^a UNIVERSIDAD DE TARAPACÁ   (Chile)

^b UNIVERSIDAD DEL BÍO BÍO   (Chile)

^c FEDERAL UNIVERSITY OF MARANHÃO   (Brazil)

^d UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Decomposition of fuzzy sets; Extension principle; Fuzzy sets

Indexed keywords

APPROXIMATION ALGORITHMS; BOUNDARY CONDITIONS; FUNCTION EVALUATION; LINEARIZATION;

EXTENSION PRINCIPLE; MULTILINEARIZATION;

FUZZY SETS;

EID: 34548733039     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.07.007     Document Type: Article

References (21)

1. Barros L.C., Bassanezi R.C., and Tonelli P.A. Fuzzy modelling in population dynamics. Ecological Modelling 128 (2000) 27-33
2. Cabrelli C.A., Forte B., Molter U.M., and Vrscay E.R. Iterated fuzzy sets systems: a new approach to the inverse problem for fractals and other sets. Journal of Mathematical Analysis and Applications 171 (1992) 79-100
3. Dong W.M., and Wong F.S. Fuzzy weighted average and implementation of the extension principle. Fuzzy Sets and Systems 21 (1987) 183-199
4. Hanss M. The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets and Systems 130 (2002) 277-298
5. Hanss M. Applied Fuzzy Arithmetic: An Introduction with Engineering Applications (2005), Springer Verlag, Berlin
6. Hofer E. When to separate uncertainties and when not to separate. Reliability Engineering and System Safety vol. 54 (1996), Elsevier Science
7. Hüllermeier E. An approach to modeling and simulation of uncertain dynamical systems. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 5 (1997) 117-137
8. W (the weakest t-norm)-based fuzzy arithmetic operations. Information Sciences 176 (2006) 150-160
9. Janiš V., Šešelja B., and Tepavcevic A. Non-standard cut classification of fuzzy sets. Information Sciences 177 (2007) 161-169
10. Kaufmann A., and Gupta M.M. Introduction of fuzzy arithmetic: theory and application. Electrical Computer Science and Engineering Series (1985), Van Nostrand Reinhold, New York
11. Klir G.J. Fuzzy arithmetic with requisite constrains. Fuzzy Sets and Systems 91 (1997) 165-175
12. Misukoshi M., Barros L., Chalco-Cano Y., Román-Flores H., and Bassanezi R.C. Fuzzy differential equations and the extension principle. Information Sciences 177 (2007) 3627-3635
13. Román-Flores H., Barros L., and Bassanezi R. A note on the Zadeh's extensions. Fuzzy Sets and Systems 117 (2001) 327-331
14. Saavedra O.R., Mangueira H.D., Chalco-Cano Y., and Román-Flores H. Solution of non linear fuzzy systems by decomposition of incremental fuzzy numbers. Information Sciences 177 (2007) 4462-4473
15. Liu S.-T., and Chiang K. Solving fuzzy transportation problems based on extension principle. European Journal of Operational Research 153 (2004) 661-674
16. Taheri S.M., and Azizi R. On information energy of the image fuzzy sets. Information Sciences 177 (2007) 3871-3881
17. Wood K.L., Otto K.N., and Antonsoon E.K. Engineering design calculations with fuzzy parameters. Fuzzy Sets and Systems 52 (1992) 1-20
18. Yang H.Q., Yao H., and Jones J.D. Calculating functions of fuzzy numbers. Fuzzy Sets and Systems 55 (1993) 273-283
19. Yang R., Wang Z., Heng P.A., and Leung K.S. Fuzzy numbers and fuzzification of the Choquet integral. Fuzzy Sets and Systems 153 (2005) 95-113
20. Zadeh L.A. The concept of a linguistic variable and its applications in approximate reasoning. Information Sciences 8 (1975) 199-251
21. Zadeh L.A. Toward a generalized theory of uncertainty (GTU) - an outline. Information Sciences 172 (2005) 1-40