Journal:Informatica
Volume 32, Issue 3 (2021), pp. 543–564
Abstract
As an extension of intuitionistic fuzzy sets, picture fuzzy sets can deal with vague, uncertain, incomplete and inconsistent information. The similarity measure is an important technique to distinguish two objects. In this study, a similarity measure between picture fuzzy sets based on relationship matrix is proposed. The new similarity measure satisfies the axiomatic definition of similarity measure. It can be testified from a numerical experiment that the new similarity measure is more effective. Finally, we apply the proposed similarity measure to multiple-attribute decision making.
Journal:Informatica
Volume 26, Issue 2 (2015), pp. 241–258
Abstract
Abstract
We propose a normalized parameter for characterization of similarity/dissimilarity of two sequences providing a smoothly varying measure for varying symmetry score. Such a parameter can be used for analysis of experimental data and fitting to a theoretical model, mirror symmetry estimation with respect to a selected or presumed symmetry axis, in particular, in symmetry detection applications where the selected symmetry parameters must be evaluated multiple times. We compare the proposed parameter, as well as several of the well-known distance and similarity measures, on an ensemble of template functions morphing continuously from symmetric to antisymmetric shape. This comparison allows to evaluate different similarity and symmetry measures in a more controlled and systematic setting than a simple visual estimation in sample images.