Soft Computing

, Volume 5, Issue 3, pp 179–193

Interpolation and extrapolation of fuzzy quantities revisited – an axiomatic approach

  • S. Jenei
Original paper

DOI: 10.1007/s005000100080

Cite this article as:
Jenei, S. Soft Computing (2001) 5: 179. doi:10.1007/s005000100080

Abstract

 This paper deals with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-Then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. The methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. For this purpose the well-known fuzzy inference mechanisms have to be extended or replaced by more general ones. The methods proposed so far in the literature for rule interpolation are mainly conceived for the application to fuzzy control and miss certain logical characteristics of an inference. First, a set of axioms is proposed in this paper. With this, a definition is given for the notion of interpolation, extrapolation, linear interpolation and linear extrapolation of fuzzy rules. The axioms include all the conditions that have been of interest in the previous attempts and others which either have logical characteristics or try to capture the linearity of the interpolation. A new method for linear interpolation and extrapolation of compact fuzzy quantities of the real line is suggested and analyzed in the spirit of the given definition. The method is extended to non-linear interpolation and extrapolation as well.

Keywords Fuzzy quantity (linear) Interpolation/extrapolation Sparse rule-base Approximate reasoning Expert system Fuzzy control 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • S. Jenei
    • 1
  1. 1.Institute of Mathematics and Information, University of Pécs, Ifjúság u. 6, H-7624 Pécs, Hungary E-mail: jenei@ttk.pte.hu, Department of Algebra, Stochastic and Knowledge-Based Mathematical Systems, Johannes Kepler University, A-4040 Linz, Austria E-mail: sandor@f111.uni-linz.ac.atAT

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