A new fuzzy interpolative reasoning method based on center of gravity

Interpolative reasoning methods do not only help reduce the complexity of fuzzy models but also make inference in sparse-rule based systems possible. This paper presents an interpolative reasoning method by exploiting the center of gravity (COG) property of the fuzzy sets concerned. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using similarity information to convert the intermediate inference results into the final derived conclusion. Two transformation operations are introduced to support such reasoning, which allow the COG of a fuzzy set to remain unaltered before and after the transformation. Results of experimental comparisons are provided to reflect the success of this work.