Threshold generation method of construction of a new implication from two given ones

In this paper, a new construction method of a fuzzy implication from two given ones, called threshold generation method, is introduced. It is a generalization of the way of construction of the recently introduced h-implications, which are fully characterized in this work. The threshold generation method allows to control, up to a certain level, the increasingness on the second variable of the fuzzy implication through an adequate scaling on that variable of the two given implications. The natural propagation of the most usual properties of fuzzy implications from the initial ones to the constructed implication is studied and the necessary and sufficient conditions in order to ensure this propagation are presented. In particular, the preservation of the contrapositive symmetry on threshold generated implications needs of another construction method of a fuzzy implication from a given one and a fuzzy negation.

[1]  Rudolf Kruse,et al.  Information Processing and Management of Uncertainty in Knowledge-Based Systems , 2011 .

[2]  Haris N. Koutsopoulos,et al.  Models for route choice behavior in the presence of information using concepts from fuzzy set theory and approximate reasoning , 1993 .

[3]  Joan Torrens,et al.  Continuous R-implications generated from representable aggregation functions , 2010, Fuzzy Sets Syst..

[4]  Joan Torrens,et al.  S- and R-implications from uninorms continuous in ]0, 1[2 and their distributivity over uninorms , 2009, Fuzzy Sets Syst..

[5]  Feng Qin,et al.  Solutions to the functional equation I(x, y)=I(x, I(x, y)) for a continuous D-operation , 2010, Inf. Sci..

[6]  Joan Torrens,et al.  Residual implications and co-implications from idempotent uninorms , 2004, Kybernetika.

[7]  Joan Torrens,et al.  Two types of implications derived from uninorms , 2007, Fuzzy Sets Syst..

[8]  Joan Torrens,et al.  The law of importation for discrete implications , 2009, Inf. Sci..

[9]  Ronald R. Yager,et al.  On some new classes of implication operators and their role in approximate reasoning , 2004, Inf. Sci..

[10]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

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

[12]  Guoqing Chen,et al.  Discovering a cover set of ARsi with hierarchy from quantitative databases , 2005, Inf. Sci..

[13]  Joan Torrens,et al.  A characterization of (U, N), RU, QL and D-implications derived from uninorms satisfying the law of importation , 2010, Fuzzy Sets Syst..

[14]  Marc Roubens,et al.  Fuzzy Preference Modelling and Multicriteria Decision Support , 1994, Theory and Decision Library.

[15]  Michal Baczynski,et al.  Fuzzy Implications , 2008, Studies in Fuzziness and Soft Computing.

[16]  Joan Torrens,et al.  On the representation of fuzzy rules , 2008, Int. J. Approx. Reason..

[17]  J. Fodor Contrapositive symmetry of fuzzy implications , 1995 .

[18]  Joan Torrens,et al.  A Survey on Fuzzy Implication Functions , 2007, IEEE Transactions on Fuzzy Systems.

[19]  S. Gottwald A Treatise on Many-Valued Logics , 2001 .

[20]  Joan Torrens,et al.  Distributivity of residual implications over conjunctive and disjunctive uninorms , 2007, Fuzzy Sets Syst..

[21]  Michal Baczynski,et al.  Yager's classes of fuzzy implications: some properties and intersections , 2007, Kybernetika.

[22]  Bernard De Baets,et al.  Residual operators of uninorms , 1999, Soft Comput..

[23]  Joan Torrens,et al.  A characterization of residual implications derived from left-continuous uninorms , 2010, Inf. Sci..

[24]  B. Jayaram On the Law of Importation , 2008 .

[25]  Joan Torrens,et al.  On some properties of threshold generated implications , 2012, Fuzzy Sets Syst..

[26]  Humberto Bustince,et al.  Definition and construction of fuzzy DI-subsethood measures , 2006, Inf. Sci..

[27]  Humberto Bustince,et al.  Construction of fuzzy indices from fuzzy DI-subsethood measures: Application to the global comparison of images , 2007, Inf. Sci..

[28]  Balasubramaniam Jayaram,et al.  (U, N)-Implications and Their Characterizations , 2009, EUSFLAT Conf..

[29]  Yun Shi,et al.  On the characterizations of fuzzy implications satisfying I(x, y)=I(x, I(x, y)) , 2007, Inf. Sci..

[30]  Hua-Wen Liu,et al.  Solutions to the functional equation I(x, y) = I(x, I(x, y)) for three types of fuzzy implications derived from uninorms , 2012, Inf. Sci..

[31]  Balasubramaniam Jayaram,et al.  On the Law of Importation $(x \wedge y) \longrightarrow z \equiv (x \longrightarrow (y \longrightarrow z))$ in Fuzzy Logic , 2008, IEEE Transactions on Fuzzy Systems.

[32]  Joan Torrens,et al.  Some Remarks on the Solutions to the Functional Equation I(x, y) = I(x, I(x, y)) for D-Operations , 2010, IPMU.

[33]  J. Balasubramaniam,et al.  Contrapositive symmetrisation of fuzzy implications - Revisited , 2006, Fuzzy Sets Syst..

[34]  Joan Torrens,et al.  The law of importation versus the exchange principle on fuzzy implications , 2011, Fuzzy Sets Syst..

[35]  Joan Torrens,et al.  On a new class of fuzzy implications: h-Implications and generalizations , 2011, Inf. Sci..

[36]  Michal Baczynski,et al.  (S, N)- and R-implications: A state-of-the-art survey , 2008, Fuzzy Sets Syst..

[37]  E. Trillas,et al.  On the law [p/spl and/q/spl rarr/r]=[(p/spl rarr/r)V(q/spl rarr/r)] in fuzzy logic , 2002 .

[38]  Enric Trillas,et al.  On the law [p∧q→r]=[(p→r)V(q→r)] in fuzzy logic , 2002, IEEE Trans. Fuzzy Syst..

[39]  J. Balasubramaniam,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[40]  R. Mesiar,et al.  Conjunctors and their Residual Implicators: Characterizations and Construction Methods , 2007 .

[41]  Radko Mesiar,et al.  Fuzzy Equivalence Relations and Fuzzy Partitions , 2009, J. Multiple Valued Log. Soft Comput..