The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features
Robert K. Nowicki, Bartosz A. Nowak, Janusz T. Starczewski, Krzysztof Cpałka · 2014
The architecture of neuro-fuzzy systems with fuzzy rough sets originally has been developed to process with imprecise data. In this paper, the adaptation of those systems to the missing features case is presented. However, the main considerations concern with methods of learning which could be applied to such systems for approximation tasks. Various methods for determining values of system parameters have been considered, in particular the gradient learning method. The effectiveness of proposed methods has been confirmed by many simulation experiments, which results have been supplied to this paper.