Finite-valued indistinguishability operators

Fuzzy equality relations or indistinguishability operators generalize the concepts of crisp equality and equivalence relations in fuzzy systems where inaccuracy and uncertainty is dealt with. They generate fuzzy granularity and are an essential tool in Computing with Words (CWW). Traditionally, the degree of similarity between two objects is a number between 0 and 1, but in many occasions this assignment cannot be done in such a precise way and the use of indistinguishability operators valued on a finite set of linguistic labels such as small, very much, etc. would be advisable. Recent advances in the study of finite-valued t-norms allow us to combine this kind of linguistic labels and makes the development of a theory of finite-valued indistinguishability operators and their application to real problems possible.

[1]  B. De Baets,et al.  Transitive approximation of fuzzy relations by alternating closures and openings , 2003 .

[2]  Petr Cintula,et al.  Valverde-Style Representation Results in a Graded Framework , 2007, EUSFLAT Conf..

[3]  G. Mayor,et al.  Triangular norms on discrete settings , 2005 .

[4]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[5]  J. Jacas Similarity relations: the calculation of minimal generating families , 1990 .

[6]  Bernard De Baets,et al.  UPGMA clustering revisited: A weight-driven approach to transitive approximation , 2006, Int. J. Approx. Reason..

[7]  Radko Mesiar,et al.  Triangular Norms , 2000, Trends in Logic.

[8]  Enric Hernández,et al.  Indistinguishability relations in Dempster-Shafer theory of evidence , 2004, Int. J. Approx. Reason..

[9]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[10]  Ramón González del Campo,et al.  An algorithm to compute the transitive closure, a transitive approximation and a transitive opening of a fuzzy proximity , 2009, SOCO 2009.

[11]  Joan Torrens,et al.  Modus ponens and modus tollens in discrete implications , 2008, Int. J. Approx. Reason..

[12]  J. Recasens,et al.  Fuzzy T-transitive relations: eigenvectors and generators , 1995 .

[13]  Frank Klawonn,et al.  Similarity in fuzzy reasoning , 1995 .

[14]  Joan Torrens,et al.  On two types of discrete implications , 2005, Int. J. Approx. Reason..

[15]  U. Höhle,et al.  Applications of category theory to fuzzy subsets , 1992 .

[16]  Joan Torrens,et al.  QL-implications on a finite chain , 2003, EUSFLAT Conf..

[17]  J. Recasens,et al.  Fuzzy Equivalence Relations: Advanced Material , 2000 .

[18]  R. Mesiar,et al.  Logical, algebraic, analytic, and probabilistic aspects of triangular norms , 2005 .

[19]  Sebastian García Galán,et al.  Fuzzy controller applications in stand-alone photovoltaic systems , 2002 .

[20]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[21]  Bernard De Baets,et al.  The complete linkage clustering algorithm revisited , 2005, Soft Comput..

[22]  Berthold Schweizer,et al.  Probabilistic Metric Spaces , 2011 .

[23]  Petr Cintula,et al.  Relations in Fuzzy Class Theory: : Initial steps , 2008, Fuzzy Sets Syst..

[24]  D. Dubois,et al.  Fundamentals of fuzzy sets , 2000 .

[25]  U. Höhle M-valued Sets and Sheaves over Integral Commutative CL-Monoids , 1992 .

[26]  L. Valverde On the structure of F-indistinguishability operators , 1985 .

[27]  Marc Roubens,et al.  Structure of transitive valued binary relations , 1995 .