The concept of hierarchical models of associations of fuzzy sets (linguistic labels) is discussed. Three basic levels of hierarchy (relational, set-theoretic, and scalar) facilitate the handling of a variety of relationships between fuzzy sets. Learning mechanisms capable of discovering parameters of the models introduced are studied. The inverse problem in models of associations is formulated along with a construction of diverse forms of matching achieved there. An illustrative numerical example in pattern classification is also presented. >
['Witold Pedrycz']
Associations of fuzzy sets