DocumentCode
126890
Title
Fast learning method of interval type-2 fuzzy neural networks
Author
Olczyk, Damian ; Markowska-Kaczmar, Urszula
Author_Institution
Wroclaw Univ. of Technol., Wroclaw, Poland
fYear
2014
fDate
8-10 Sept. 2014
Firstpage
1
Lastpage
6
Abstract
The Fuzzy Set Parameter Estimation algorithm is proposed for fast learning interval type-2 fuzzy neural networks applied for classification problems. Classes are disjoint. Learning consists of estimating appropriate values of fuzzy set parameters in every rule. Estimation is based on statistical properties of the training data. The experimental study confirms that it is dozens times quicker than the backpropagation method, while the classification effectiveness is comparable.
Keywords
fuzzy logic; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); parameter estimation; pattern classification; statistical analysis; appropriate value estimation; classification problems; fast learning interval type-2 fuzzy neural networks; fast learning method; fuzzy logic type-1; fuzzy set parameter estimation algorithm; training data statistical properties; Backpropagation; Clustering algorithms; Fuzzy neural networks; Neurons; Training; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence (UKCI), 2014 14th UK Workshop on
Conference_Location
Bradford
Type
conf
DOI
10.1109/UKCI.2014.6930169
Filename
6930169
Link To Document