Fuzzy rule base interpolation based on semantic revision

Sometimes it is not possible to have a full dense rule base as there are gaps in the information. Furthermore, it is often necessary to deal with a sparse rule base to reduce the size and the inference/control time. In such sparse rule bases classic algorithms such as the CRI of Zadeh (1973) and the Mamdani method do not function for observations hitting gaps between rules. A linear fuzzy rule interpolation technique (KH-interpolation) has been introduced that is suitable for dealing with sparse bases. However, this method often results in conclusions which are not directly interpretable. In this paper an interpolation technique is proposed that is based on the interpolation of the semantics and interrelation of rules. This method guarantees the direct interpretability of the conclusion