R-norm intuitionistic fuzzy information measures and its computational applications

Abstract

Atanassov introduced the concept of intuitionistic fuzzy sets (IFS), as a generalization of fuzzy sets, which is capable of capturing the information that includes some degree of hesitation. In the present communication, a new R-norm intuitionistic fuzzy entropy and a weighted R-norm intuitionistic fuzzy directed divergence measure have been proposed with their proof of validity. Further, empirical study on the proposed information measures has also been done which explains monotonic nature of the information measures with respect to R and the weight. Computational applications of these information measures in the field of pattern recognition and image thresholding has been proposed with discussion. © 2012 Springer-Verlag.