A comparison of Mamdani and Sugeno fuzzy inference systems for chaotic time series prediction

Predicting chaotic time series by fuzzy inference systems is one of active research areas. This paper concerns Mamdani and Sugeno fuzzy inference systems in the application of chaotic time series prediction. These two types of fuzzy inference systems were compared through four sets of experiments on a number of chaotic time series to evaluate their prediction performance in terms of model generalization, execution time, structure complexity, and noise tolerance. The experimental results indicate that it may as well choose Sugeno fuzzy inference system as a predictor in the first place for the situations where the training data are not severely corrupted by noise.