Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34636
Title: T2FELA : type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic system
Authors: Deng, Z
Choi, KS 
Cao, L
Wang, S
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2014, v. 25, no. 4, p. 664-676 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities.
URI: http://hdl.handle.net/10397/34636
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2013.2280171
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