On a non-nested level-based representation of fuzziness

  • Authors:

  • D. Sánchez;M. Delgado;M. A. Vila;J. Chamorro-Martínez

  • Affiliations:

  • European Centre for Soft Computing, Edificio Científico-Tecnológico. 33600 Mieres, Asturias, Spain and Department of Computer Science and Artificial Intelligence, University of Granada C ...;Department of Computer Science and Artificial Intelligence, University of Granada C/ Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada C/ Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada C/ Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 2012

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Abstract

In this paper we describe a non-nested level-based representation of fuzziness, closely related to some existing models and concepts in the literature. Our objective is to motivate the use of this non-nested model by describing its theoretical possibilities, and illustrating them with some existing applications. From a theoretical point of view, we discuss the semantics of the representation, which goes beyond and has as a particular case fuzzy sets as represented by a collection of @a-cuts. In addition, the proposed operations on level-based representations, contrary to those of existing fuzzy set theories, satisfy all the properties of Boolean logic. We discuss the contributions of the representation and operation by levels to practical applications, in particular for extending crisp notions to the fuzzy case. In this respect, an important contribution of the proposal is that fuzzy mathematical objects (not only sets and the corresponding predicates) and operations are uniquely and easily defined as extensions of their crisp counterparts. In order to illustrate this claim, we recall level representations of quantities (gradual numbers) and their complementarity to fuzzy intervals (often inappropriately called fuzzy numbers).