Max-min fuzzy neural networks for solving relational equations
- Blanco Ferro, Antonio
- Delgado Calvo-Flores, Miguel
- Requena Ramos, Ignacio
ISSN: 1134-5632
Ano de publicación: 1994
Volume: 1
Número: 3
Páxinas: 335-345
Tipo: Artigo
Outras publicacións en: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology
Resumo
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.