Abstract
This paper proposes the type-2 fuzzy neural network system (type-2 FNN) which combines the advantages of type-2 fuzzy logic systems (FLSs) and neural networks (NNs). For considering the system uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The previous results of type-1 FNN systems can be extended to a type-2 one. Furthermore, the corresponding learning algorithm is derived by input-to-state-stability (ISS) approach. Nonlinear system identification is presented to illustrate the effectiveness of our approach.
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