Abstract
The paper proposes an optimization environment for decomposing fuzzy relations. Two basic categories of approaches are developed: (i) based on direct gradient-oriented optimization methods, and (ii) exploiting the ideas of fuzzy neurocomputation. These two approaches are carefully analyzed and contrasted making use of illustrative numerical material. Some immediate applications to pattern classification and fuzzy interpolation are studied in detail. The study includes also some generalizations of the generic decomposition problem.