Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system Online publication date: Thu, 26-Jan-2012
by Robert Czabanski; Michal Jezewski; Janusz Jezewski; Janusz Wrobel; Krzysztof Horoba
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 6, No. 1, 2012
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.
Online publication date: Thu, 26-Jan-2012
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org