Title :
A training technique for fuzzy number neural networks
Author :
Dunyak, James ; Wunsch, Donald
Author_Institution :
Dept. of Math., Texas Tech. Univ., Lubbock, TX, USA
Abstract :
A new technique is discussed for the training of fuzzy neural networks with general fuzzy number inputs, weights, and outputs. Fuzzy number neural networks are difficult to train because of the many alpha-cut constraints implied by the fuzzy weights. In this paper, an unconstrained representation is used for the fuzzy weights, allowing application of a standard backpropagation approach. The technique is demonstrated on a moderately large problem
Keywords :
fuzzy neural nets; learning (artificial intelligence); alpha-cut constraints; fuzzy number neural networks; standard backpropagation approach; training technique; unconstrained representation; Constraint optimization; Constraint theory; Equations; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Integrated circuit noise; Mathematics; Neural networks; Neurons;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.611725
