Identification and control of nonlinear systems using type-2 fuzzy set based neuro-fuzzy model

A novel method for modeling and identification of a nonlinear system using Neuro-Fuzzy model based on type-2 fuzzy sets is proposed. The model can handle uncertainties in the rules arising out of type-2 fuzzy sets that bear variation in the membership functions. The approach involves the operations of fuzzification, inference and output processing for finding the number of rules whereas the defuzzification operation is done by neural network. The neuro-fuzzy Model derived using type-2 fuzzy sets is used for both identification and control of nonlinear systems very efficiently. It is demonstrated that type-2 fuzzy logic systems (FLS) are more effective over type-1 FLS for handling uncertainties.