Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction
Eyoh, Imo and John, Robert and de Maere, Geert (2016) Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), 9-12 October 2016, Budapest, Hungary.
This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parame- ters of the IT2IFLS. The empirical comparison is made on the designed system using two synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in terms of non-membership functions and hesitation indexes in IT2IFLS tend to reduce the root mean square error (RMSE) of the system compared to a type-1 fuzzy logic approach and some interval type-2 fuzzy systems.
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