Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system

Authors:
Robert Czabanski;Michal Jezewski;Janusz Jezewski;Janusz Wrobel;Krzysztof Horoba
Affiliations:
Institute of Electronics, Silesian University of Technology, ul. Akademicka 16, 44-101 Gliwice, Poland.;Institute of Electronics, Silesian University of Technology, ul. Akademicka 16, 44-101 Gliwice, Poland.;Department of Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland.;Department of Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland.;Department of Signal Processing, Institute of Medical Technology and Equipment, ul. Roosevelta 118, 41-800 Zabrze, Poland
Venue:
International Journal of Intelligent Information and Database Systems
Year:
2012

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Abstract

The paper presents a method of parameters estimation for artificial neural network based on fuzzy inference system (ANNBFIS). It is based on deterministic annealing, ε-insensitive learning by solving a system of linear inequalities and robust fuzzy c-means clustering. The proposed algorithm allows to improve the neuro-fuzzy modelling quality by increasing the generalisation ability and outliers robustness. To find the unknown number of fuzzy rules we proposed the procedure of robust clusters merging. The performance of the learning method is demonstrated through the benchmark sunspot prediction problem.