Methodology for handling uncertainty by using interval type-2 Fuzzy Logic systems

This paper proposes a methodology that is useful for handling uncertainty in non-linear systems by using type-2 Fuzzy Logic (FL). This methodology works under a training scheme from numerical data, using type-2 Fuzzy Logic Systems (FLS). Different training methods can be applied while working with it, as well as different training approaches. One of the training methods used here is also a proposal -the One-Pass method for interval type-2 FLS. We accomplished several experiments forecasting a chaotic time-series with an additive noise and obtained better performance with interval type-2 FLSs than with conventional ones. In addition, we used the designed FLSs to forecast the time-series with different initial conditions, and it did not affect their performance.