Optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with Genetic Algorithms

This paper describes the optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass to test the experiments. The methods used for the integration of the ensembles of ANFIS are: type-1 and interval type-2 fuzzy inference system (FIS) of the Mamdani kind. The Genetic Algorithms (GAs) are used for the optimization of memberships function parameters of FIS in each integrator. In the experiments we changed the type of membership functions to each type-1 and interval type-2 FIS, thereby increasing the complexity of the training, The output (Forecast) generated of each integrators is calculated with RMSE (root mean square error) to minimize the prediction error, therefore we compared the performance obtained of each FIS.