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
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;
Conference_Titel :
Computational Intelligence (UKCI), 2014 14th UK Workshop on
Conference_Location :
Bradford
DOI :
10.1109/UKCI.2014.6930169
