A short note on fuzzy relational inference systems

This paper is a short note contribution to the topic of fuzzy relational inference systems and the preservation of their desirable properties. It addresses the two main fuzzy relational inferences – compositional rule of inference (CRI) and the Bandler–Kohout subproduct (BK-subproduct) – and their combination with two fundamental fuzzy relational models of fuzzy rule bases, namely, the Mamdani–Assilian and the implicative models. The goal of this short note article is twofold. Firstly, we show that the robustness related to the combination of BK-subproduct and implicative fuzzy rule base model was not proven correctly in [24]. However, we will show that the result itself is still valid and a valid proof will be provided. Secondly, we shortly discuss the preservation of desirable properties of fuzzy inference systems and conclude that neither the above mentioned robustness nor any other computational advantages should automatically lead to a preference of the combinations of CRI with Mamdani–Assilian models or of the BK-subproduct with the implicative models.

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

[2]  Martin Stepnicka,et al.  Conditionally Firing Implicative Rules , 2015, IFSA-EUSFLAT.

[3]  George J. Klir,et al.  Fuzzy sets and fuzzy logic , 1995 .

[4]  Mirko Navara,et al.  How to Use Controller with Conditionally Firing Rules , 2007, EUSFLAT Conf..

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

[6]  Stephan Lehmke,et al.  Correct models of fuzzy IF-THEN rules are continuous , 2006, Fuzzy Sets Syst..

[7]  Yan Shi,et al.  Reasoning conditions on Kóczy's interpolative reasoning method in sparse fuzzy rule bases. Part II , 1997, Fuzzy Sets Syst..

[8]  L. Nosková,et al.  University of Ostrava Institute for Research and Applications of Fuzzy Modeling System of fuzzy relation equations with inf → composition : solvability and solutions , 2005 .

[9]  Ladislav J. Kohout,et al.  Semantics of implication operators and fuzzy relational products , 1980 .

[10]  Vilém Novák,et al.  A Plea for the Usefulness of the Deductive Interpretation of Fuzzy Rules in Engineering Applications , 2007, 2007 IEEE International Fuzzy Systems Conference.

[11]  I. Burhan Türksen,et al.  An approximate analogical reasoning approach based on similarity measures , 1988, IEEE Trans. Syst. Man Cybern..

[12]  Bernard De Baets,et al.  University of Ostrava Institute for Research and Applications of Fuzzy Modeling Arithmetic Fuzzy Models , 2010 .

[13]  Chee Seng Chan,et al.  A weighted inference engine based on interval-valued fuzzy relational theory , 2015, Expert Syst. Appl..

[14]  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..

[15]  Elie Sanchez,et al.  Resolution of Composite Fuzzy Relation Equations , 1976, Inf. Control..

[16]  Martin Stepnicka,et al.  Interpolativity of at-least and at-most models of monotone single-input single-output fuzzy rule bases , 2013, Inf. Sci..

[17]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[18]  Martin Stepnicka,et al.  On Additive and Multiplicative Fuzzy Models , 2007, EUSFLAT Conf..

[19]  R. Belohlávek Fuzzy Relational Systems: Foundations and Principles , 2002 .

[20]  Martin Stepnicka,et al.  On the Suitability of the Bandler–Kohout Subproduct as an Inference Mechanism , 2010, IEEE Transactions on Fuzzy Systems.

[21]  B. Baets Analytical solution methods for fuzzy relational equations. , 2000 .

[22]  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.

[23]  Petr Hájek,et al.  A Note on Fuzzy Inference as Deduction , 1999 .

[24]  Vilém Novák,et al.  Logical structure of fuzzy IF-THEN rules , 2006, Fuzzy Sets Syst..

[25]  F. Klawonn Fuzzy points, fuzzy relations and fuzzy functions , 2000 .

[26]  Siegfried Gottwald,et al.  Generalized solvability behaviour for systems of fuzzy equations , 2000 .

[27]  Mirko Navara,et al.  Fuzzy controllers with conditionally firing rules , 2002, IEEE Trans. Fuzzy Syst..

[28]  W. Pedrycz Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data , 1985 .

[29]  Radko Mesiar,et al.  Compositional rule of inference as an analogical scheme , 2003, Fuzzy Sets Syst..

[30]  László T. Kóczy,et al.  Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases , 1993, Inf. Sci..

[31]  W. Pedrycz,et al.  Fuzzy Relation Equations and Their Applications to Knowledge Engineering , 1989, Theory and Decision Library.

[32]  Henri Prade,et al.  What are fuzzy rules and how to use them , 1996, Fuzzy Sets Syst..

[33]  Martin Stepnicka,et al.  On the satisfaction of Moser-Navara axioms for fuzzy inference systems , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[34]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[35]  V. Novák,et al.  Mathematical Principles of Fuzzy Logic , 1999 .

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

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

[38]  Martin Stepnicka,et al.  Interpolativity of at-least and at-most models of monotone fuzzy rule bases with multiple antecedent variables , 2016, Fuzzy Sets Syst..

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

[40]  Siegfried Gottwald,et al.  An Abstract Approach Toward the Evaluation of Fuzzy Rule Systems , 2007 .

[41]  Balasubramaniam Jayaram,et al.  Bandler-Kohout Subproduct With Yager's Classes of Fuzzy Implications , 2014, IEEE Trans. Fuzzy Syst..