Weighted similarity measure on interval-valued fuzzy sets and its application to pattern recognition

A new approach to define the similarity measure between interval-valued fuzzy sets is presented. The proposed approach is based on a weighted measure in which the normalized similarities between lower functions and also between upper functions are combined by a weight parameter. The properties of this similarity measure are investigated. It is shown that, the proposed measure has some advantages in comparison with the commonly used similarity measures.