Applications of type-2 fuzzy logic systems to forecasting of time-series
In this paper, we begin with a type-1 fuzzy logic system (FLS), trained with noisy data. We then demonstrate how information about the noise in the training data can be incorporated into a type-2 FLS, which can be used to obtain bounds within which the true (noisefree) output is likely to lie. We do this with the example of a one-step predictor for the MackeyGlass chaotic time-series [M.C. Mackey, L. Glass, Oscillation and chaos in physiological control systems, Science 197 (1977) 287280]. We also demonstrate how a type-2 FLS can be used to obtain better predictions than those obtained with a type-1 FLS.
fuzzy logic systems forecasting time-series type-2 fuzzy sets rule uncertainties