by Janusz Starczewski, Rafał Scherer, Marcin Korytkowski, Robert Nowicki
Abstract:
In the paper we study a modular system which can be converted into a type-2 neuro-fuzzy system. The rule base of such system consists of triangular type-2 fuzzy sets. The modular structure is trained using the backpropagation method combined with the AdaBoost algorithm. By applying the type-2 neuro-fuzzy system, the modular structure is converted into a compressed form. This allows to overcome the training problem of type-2 neuro-fuzzy systems. An illustrative example is given to show the efficiency of our approach in the problems of classification.
Reference:
J. Starczewski, R. Scherer, M. Korytkowski, R. Nowicki, "Modular Type-2 Neuro-fuzzy Systems", Lecture Notes in Computer Science, vol. 4967, 2008, pp. 570-578.
Bibtex Entry:
@ARTICLE{StarSchKorytNow_PPAM2008,
author = {Janusz Starczewski and Rafał Scherer and Marcin Korytkowski and Robert
Nowicki},
title = {Modular Type-2 Neuro-fuzzy Systems},
journal = {Lecture Notes in Computer Science},
year = {2008},
volume = {4967},
pages = {570-578},
abstract = {In the paper we study a modular system which can be converted into
a type-2 neuro-fuzzy system. The rule base of such system consists
of triangular type-2 fuzzy sets. The modular structure is trained
using the backpropagation method combined with the AdaBoost algorithm.
By applying the type-2 neuro-fuzzy system, the modular structure
is converted into a compressed form. This allows to overcome the
training problem of type-2 neuro-fuzzy systems. An illustrative example
is given to show the efficiency of our approach in the problems of
classification.},
url = {http://www.springerlink.com/content/c412v2479u38033h/}
}