Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)

Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural networks as the consequent part. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process. It can is also shown that both learning rates cannot be both negative. Excellent results are obtained for the truck backing-up control, which yield more improved performance than those using type-1 FNN.