Transformation Based Interpolation with Generalized Representative Values
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Date
2005Author
Huang, Zhiheng
Shen, Qiang
Metadata
Abstract
Fuzzy interpolation offers the potential to model
problems with sparse rule bases, as opposed to dense rule
bases deployed in traditional fuzzy systems. It thus supports the
simplification of complex fuzzy models and facilitates inferences
when only limited knowledge is available. This paper first
introduces the general concept of representative values (RVs),
and then uses it to present an interpolative reasoning method
which can be used to interpolate fuzzy rules involving arbitrary
polygonal fuzzy sets, by means of scale and move transformations.
Various interpolation results over different RV implementations
are illustrated to show the flexibility and diversity of this
method. A realistic application shows that the interpolation-based
inference can outperform the conventional inferences.