A Novel Algorithm for Tuning of the Type-2 Fuzzy System

Type-2 fuzzy logic systems (FLSs) let uncertainties that occur in rule-based FLSs be modeled using the new third dimension of type-2 fuzzy sets. Although a complete theory of type-2 FLSs exists for general type-2 fuzzy sets, it is only for interval type-2 fuzzy sets that type-2 FLSs are practical. Type 2 fuzzy sets allow for linguistic grades of membership. A type-2 fuzzy inferencing systems uses type-2 fuzzy sets to represent uncertainty in both the representation and inferencing. However; as with type-l fuzzy systems there is till an issue with regard to the design of the appropriate membership functions. One of the best performance the fuzzy inference system is optimized by the least square and numerical method .The key advantages of the least square method are the efficient use of samples and the simplicity of the implementation but it can be take long time for convergence. This paper presents a novel type-2 adaptive system for learning the membership grades of type-2 fuzzy sets which can be important. The results from the application problems lead us to believe that this approach offers the capability to allow linguistic descriptors to be learnt by an adaptive network and we can use some new algorithm same as Reinforcement Learning Methods for adaptation.

کلیدواژه ها:

Fuzzy Decision ، Type 2 Fuzzy Sets ، Adaptive Fuzzy Systems ، FOU