Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Montalvo, G., Soto, R. (2004). Methodology for Handling Uncertainty by Using Interval Type-2 Fuzzy Logic Systems. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_55
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DOI: https://doi.org/10.1007/978-3-540-24694-7_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
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