Solving fuzzy relational equations by max-min neural networks

Author

Blanco, A. ; Delgado, M. ; Requena, I.

Author_Institution

Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain

fYear

1994

fDate

26-29 Jun 1994

Firstpage

1737

Abstract

The problem of identifying a fuzzy system has been faced from several points of view which include statistical methods, neural networks and relational equation-solving approaches. In this paper, we present the use of a neural network without any activation function in order to identify a fuzzy system through the solution of a fuzzy relational equation from a set of examples. The main contribution of this work is to define a “smooth derivative” to be used in the minimization of the energy function which drives the learning procedure. Some examples show the effectiveness of this new approach

Keywords

equations; fuzzy systems; identification; learning (artificial intelligence); minimax techniques; minimisation; neural nets; relational algebra; energy function minimization; equation-solving; fuzzy relational equations; fuzzy system identification; learning procedure; max-min composition; max-min neural networks; smooth derivative; statistical methods; Artificial neural networks; Differential equations; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Learning systems; Network topology; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1896-X

Type

conf

DOI

10.1109/FUZZY.1994.343594

Filename

343594