Title :
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
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;
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
DOI :
10.1109/FUZZY.1994.343594
