Title :
Representing generalized fuzzy automata in recurrent neural networks
Author :
Doostfatemeh, Mansoor ; Kremer, Stefan C.
Author_Institution :
CIS Dept, Guelph Univ., Ont., Canada
Abstract :
In this paper we present a new architecture for the representation of general fuzzy automata (GFA). It is based on second-order recurrent neural networks (2ORNN). The architecture implements the functions F1 and F2, used in GFA, into the structure of 2ORNN. The performance of this representation method is compared with a previous method for embedding fuzzy automata, and it is shown that GFA are more efficiently representable in 2ORNN.
Keywords :
automata theory; fuzzy neural nets; fuzzy set theory; knowledge representation; recurrent neural nets; 2ORNN; GFA representation; generalized fuzzy automata; neural network architecture; performance; second-order recurrent neural networks; Automata; Computational Intelligence Society; Fuzzy neural networks; Fuzzy sets; History; Intelligent networks; Iris; Neural networks; Recurrent neural networks;
Conference_Titel :
Electrical and Computer Engineering, 2004. Canadian Conference on
Print_ISBN :
0-7803-8253-6
DOI :
10.1109/CCECE.2004.1347583
