Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32627
Title: Learning of weighted fuzzy production rules based on fuzzy neural network
Authors: Huang, DM
Ha, MH
Li, XF
Tsang, ECC
Li, YM
Keywords: Backpropagation
Fuzzy neural nets
Fuzzy set theory
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, 18-21 August 2005, Guangzhou, China, v. 5, p. 2901-2906 How to cite?
Abstract: In this paper, we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to refine or tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs by including local and global weights. By experimenting our method with some existing benchmark examples, the proposed method is found have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is greatly reduced.
URI: http://hdl.handle.net/10397/32627
ISBN: 0-7803-9091-1
DOI: 10.1109/ICMLC.2005.1527438
Appears in Collections:Conference Paper

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