Learning the Weights of Weighted Fuzzy If-Then Rules Via Training T-S Norms Neural Network
Chun-Ru Dong, Xizhao Wang, Xiaodong Dai · 2006
In this paper, an approach of learning the values of the weights in weighted fuzzy if-then rules is presented. Based on the concept of T-S norms, firstly, this paper presents the T-S norm-based fuzzy reasoning algorithm; secondly, we map a set of initial fuzzy if-then rules, in which all the weights are equal to 1.0, and the T-S norm-based fuzzy reasoning methodology into a forward fuzzy neural network, named T-S norm neural network, and the nodes of hidden layer are T norm neural cells, while the nodes of output layer are S norm neural cells; finally, we complete the training of the T-S norm neural network via a derived T-S norm BP algorithm. The experimental results have shown that our approach can learn the weights of weighted fuzzy if-then rules efficiently