Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system. (1st January 2012)

• Record Type: Journal Article
• Authors: Czabanski, Robert; Jezewski, Michal; Jezewski, Janusz; Wrobel, Janusz; Horoba, Krzysztof
• Is Part Of: International journal of intelligent information and database systems. Volume 6:Number 1(2012)
• Page Start: 77
• Page End: 92
• Publication Date: 2012-01-01
• Subjects: fuzzy rules extraction -- signal prediction -- neuro-fuzzy systems -- robust methods; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.