Tuning fuzzy systems by simulated annealing to predict time series with added noise

In this paper, a combination of fuzzy system models and simulated annealing are used to predict Mackey-Glass time series with different levels of added noise by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules under singleton and non-singleton fuzzifications for both Mamdani and Takagi-Sugeno (TSK). The results of the proposed methods are compared by their ability to handle uncertainty.