A new two-step fuzzy inference approach based on Takagi-Sugeno inference using discrete type 2 fuzzy sets

Abstract

Fuzzy system modeling (FSM) is one of the most prominent tools in order to capture the hidden behavior of highly nonlinear systems with uncertainty. In this paper, a new type 2 FSM approach is proposed in order to increase the predictive power of traditional Takagi-Sugeno fuzzy system models. One of the biggest problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, the proposed inference mechanism performs type reduction as a first step. Then, the type 1 inference mechanisms are utilized to deduce a model output for a given crisp observation.