Systems identification using type-2 fuzzy neural network (type-2 FNN) systems
Ching‐Hung Lee, Yu-Ching Lin, Wei-Yu Lai · 2004
This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLS) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-based fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.