Max-min fuzzy neural networks for solving relational equations

  1. Blanco Ferro, Antonio
  2. Delgado Calvo-Flores, Miguel
  3. Requena Ramos, Ignacio
Revista:
Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology

ISSN: 1134-5632

Any de publicació: 1994

Volum: 1

Número: 3

Pàgines: 335-345

Tipus: Article

Altres publicacions en: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology

Resum

The Relational Equations approach is one of the most usual ones for describing (Fuzzy) Systems and in most cases, it is the final expression for other descriptions. This is why the identification of Relational Equations from a set of examples has received considerable atention in the specialized literature. This paper is devoted to this topic, more specifically to the topic of max-min neural networks for identification. Three methods of learning Fuzzy Systems are developed by combining the most desirable properties of two existing ones: Sayto-Mukaidono's technique and the so called smoothed derivative technique.