Title :
Recurrent algebraic fuzzy neural networks based on fuzzy numbers
Author :
Arotaritei, Dragos
Author_Institution :
Dept. of Comput. Sci. & Eng., Aalborg Univ., Esbjerg, Denmark
Abstract :
A hybrid structure, recurrent algebraic fuzzy neural networks (RAFNN) using fully connected recurrent neural network architecture is proposed. The hybrid structure is based on neural network topology and fuzzy algebraic systems. All the operations are defined in the frame of fuzzy arithmetic using triangular fizzy numbers (usually non-symmetric). The experimental results demonstrate the capability of algorithm and the possibility to use successfully fuzzy numbers in recurrent architecture in order to acquire a dynamic behavior
Keywords :
fuzzy logic; fuzzy neural nets; recurrent neural nets; dynamic behavior; fully connected recurrent neural network architecture; fuzzy algebraic systems; fuzzy arithmetic; fuzzy numbers; hybrid structure; neural network topology; recurrent algebraic fuzzy neural networks; recurrent architecture; triangular fizzy numbers; Arithmetic; Computer architecture; Computer science; Fuzzy neural networks; Fuzzy systems; Network topology; Neural networks; Neurons; Recurrent neural networks; Transfer functions;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943646
