Robustness analysis of logic metrics on F(X)

S R was chosen to measure the perturbation of fuzzy sets since it is consistent with fuzzy reasoning.A sufficient condition under which the measure of perturbation " H R = 1 - S R " becomes a metric on F ( X ) was proposed.The distributions of isolated points in four logic metric spaces were investigated in detail.The four perturbation measurements H G , H 0 , H π and H L were compared from the viewpoint of robustness analysis. Based on the notion of logic similarity degree, logic metric spaces are constructed on the family of fuzzy subsets of the universe X by means of some regular implication operators. The structures of four specific logic metric spaces induced by four important logic implication operators are discussed and compared. It can be concluded that the logic metrics induced by Łukasiewicz implication and Goguen implication are more beneficial to the robustness analysis of fuzzy reasoning.

[1]  Keyun Qin,et al.  Robustness of fuzzy connectives and fuzzy reasoning , 2013, Fuzzy Sets Syst..

[2]  D. Dubois,et al.  Fuzzy sets in approximate reasoning. I, Inference with possibility distributions , 1991 .

[3]  Guojun Wang,et al.  On the Logic Foundation of Fuzzy Reasoning , 1999, Inf. Sci..

[4]  Guosheng Cheng,et al.  Error Estimation of Perturbations Under CRI , 2006, IEEE Transactions on Fuzzy Systems.

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

[6]  Witold Pedrycz,et al.  An approach to measure the robustness of fuzzy reasoning: Research Articles , 2005 .

[7]  Guojun Wang,et al.  On robustness of the full implication triple I inference method with respect to finer measurements , 2014, Int. J. Approx. Reason..

[8]  D. Dubois,et al.  Fuzzy sets in approximate reasoning, part 2: logical approaches , 1991 .

[9]  Kai-Yuan Cai,et al.  Robustness of fuzzy reasoning and δ-equalities of fuzzy sets , 2001, IEEE Trans. Fuzzy Syst..

[10]  Inés Couso,et al.  Similarity and dissimilarity measures between fuzzy sets: A formal relational study , 2013, Inf. Sci..

[11]  Lin Shaoyuan Analysis of Fuzzy Sliding Mode Control Systems , 2000 .

[12]  Donghui Guo,et al.  Robustness analysis of full implication inference method , 2013, Int. J. Approx. Reason..

[13]  Daowu Pei,et al.  Perturbation of fuzzy sets and fuzzy reasoning based on normalized Minkowski distances , 2012, Fuzzy Sets Syst..

[14]  Kai-Yuan Cai,et al.  δ-Equalities of fuzzy sets , 1995, Fuzzy Sets Syst..

[15]  Witold Pedrycz,et al.  An approach to measure the robustness of fuzzy reasoning , 2005, Int. J. Intell. Syst..

[16]  Zhang Xing-fang,et al.  Regular family of implication operator and its fuzzy reasoning triple I sustaining method , 2009 .

[17]  D. Dubois,et al.  Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions , 1999 .

[18]  Petr Hájek,et al.  Metamathematics of Fuzzy Logic , 1998, Trends in Logic.

[19]  Francesc Esteva,et al.  Review of Triangular norms by E. P. Klement, R. Mesiar and E. Pap. Kluwer Academic Publishers , 2003 .

[20]  Yongming Li,et al.  Approximation and robustness of fuzzy finite automata , 2008, Int. J. Approx. Reason..

[21]  Francisco Herrera,et al.  Searching for basic properties obtaining robust implication operators in fuzzy control , 2000, Fuzzy Sets Syst..

[22]  Chunquan Li,et al.  Robustness of fuzzy reasoning via logically equivalence measure , 2007, Inf. Sci..

[23]  A. Kandel,et al.  Applicability of some fuzzy implication operators , 1989 .

[24]  Pei Zheng,et al.  α-Triple I Method of Fuzzy Reasoning , 2005 .

[25]  Mingsheng Ying,et al.  Perturbation of fuzzy reasoning , 1999, IEEE Trans. Fuzzy Syst..

[26]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

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