Abstract
In this paper, a theoretical method of selecting fuzzy implication operators for the fuzzy inference sentence as “if x is A, then y is B” is presented. By applying representation theorems, thirty-two fuzzy implication operators are obtained. It is shown that the thirty-two fuzzy implication operators are generalizations of classical inference rule A→B, A c→B, A→B c and A c→B c respectively and can be divided four classes. By discussion, it is found that thirty fuzzy implication operators among 420 fuzzy implication operators presented by Li can be derived by applying representation theorems and two new fuzzy implication operators are obtained by the use of our methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems Man Cybernet 3, 28–44 (1973)
Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning(I). Information Sciences 8(2), 199–249 (1974)
Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning(II). Information Sciences 8(3), 301–357 (1974)
Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning(III). Information Sciences 9(1), 43–80 (1975)
Dubois, D., Prade, H.: Fuzzy sets in approximate reasoning, Part 1: Inference with possibility distributions. Fuzzy Sets and Systems 40(1), 143–202 (1999)
Nather, W.: On possibilistic inference. Fuzzy Sets and Systems 36, 327–337 (1990)
Xu, Y., Kerre, E.E., Ruan, D., Song, Z.: Fuzzy reasoning based on the extension principle. International Journal of Intelligent Systems 16, 467–495 (2001)
Koczy, L.T., Hirota, K.: Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases. Information Sciences 71(2), 169–201 (1993)
Wu, W.M.: Principle and Methods of Fuzzy Reasoning. Guizhou Science and Technology Press, Guiyang (1994)
Wang, G.J., Triple, I.: method of fuzzy reasoning. Science in China (Series E) 29(1), 41–53 (1999)
Wang, P.Z., Zhang, X.H., Lui, X.C., Zhang, H.M., Xu, W.: Mathematical theory of truth-valued flow inference. Fuzzy Sets and Systems 72, 221–238 (1995)
Li, H.X., You, F.: Fuzzy implication operators and their constructions. In: Zhou, Z.H., Cao, C.G. (eds.) Neural networks and their application, pp. 208–257. Tsinghua University Press (2004)
Tan, S.K., Wang, P.Z., Zhang, X.Z.: Fuzzy inference relation based on the theory of falling shadows. Fuzzy Sets and Systems 53, 179–188 (1993)
Luo, C.Z., Wang, P.Z.: Representation of Compositional Relations in Fuzzy Reasoning. Fuzzy Sets and Systems 36, 77–81 (1990)
Yuan, X.H., Li, H.X., Luo, C.Z.: New cut sets and there applications. Fuzzy System and Mathematics 11(1), 37–43 (1997)
Luo, C.Z.: Introduction to fuzzy sets. Beijing Normal University Press (1989)
Wang, G.J.: Non-classical Mathematical Logic and Approximate Reasoning. Science Press, Beijing (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, XN., Yuan, XH., Li, HX. (2009). The Theoretical Methods of Constructing Fuzzy Inference Relations. In: Cao, By., Zhang, Cy., Li, Tf. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88914-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-88914-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88913-7
Online ISBN: 978-3-540-88914-4
eBook Packages: EngineeringEngineering (R0)