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.
CITATION STYLE
Montalvo, G., & Soto, R. (2004). Methodology for handling uncertainty by using interval type-2 fuzzy logic systems. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2972, pp. 536–545). Springer Verlag. https://doi.org/10.1007/978-3-540-24694-7_55
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