Discovering Prediction Rules by a Neuro-fuzzy Modeling Framework

In this paper, we propose a neuro-fuzzy modeling framework to discover fuzzy rules and its application to predict chemical properties of ashes produced by thermo-electric generators. The framework is defined by several sequential steps in order to obtain a good predictive accuracy and the readability of the discovered fuzzy rules. First, a feature selection procedure is applied to the available data by discarding the features possessing lowest ranking in terms of their predictive power. Then, a competitive learning scheme is adopted to initialize a fuzzy rule base, which is successively refined by a neuro-fuzzy network trained on the available data. To improve accuracy, we applied the process on each ash property to be predicted, hence obtaining a set of MISO models that are both accurate and transparent, as shown by the reported experimen-