Information Theoretic fuzzy modeling for regression
This paper presents a novel, Information Theoretic Learning (ITL) method to model a fuzzy system for regression tasks that minimizes the Renyi's entropy of the error signal. An architecture based on a generalization of the well-kwon Adaptive-Network-Based Fuzzy Inference System (ANFIS) was used to perform such a modeling. The resulting method was tested on the prediction of future values for the Mackey-Glass chaotic time series. The results show that, when using the ITL cost function, the method returns better models in comparison with a Mean Squared Error (MSE)-guided cost function.