Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43001
Title: Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization
Authors: Wu, QI
Law, R 
Keywords: Fault diagnosis
FW v-SVC
Wavelet kernel function
Adaptive mutation
Gaussian mutation
Particle swarm optimization
Issue Date: 2010
Publisher: Elsevier
Source: Information sciences, 2010, v. 23, no. 1, p. 4514-4528 How to cite?
Journal: Information sciences 
Abstract: This paper proposes a robust loss function that penalizes hybrid noise (i.e., Gaussian noise, singularity points, and larger magnitude noise) in a complex fuzzy fault-diagnosis system. A mapping relationship between fuzzy numbers and crisp real numbers that allows a fuzzy sample set to be transformed into a crisp real sample set is also presented. Furthermore, the paper proposes a novel fuzzy robust wavelet support vector classifier (FRWSVC) based on a wavelet base function and develops an adaptive Gaussian particle swarm optimization (AGPSO) algorithm to seek the optimal unknown parameter of the FRWSVC. The results of experiments that apply the hybrid diagnosis model based on the FRWSVC and the AGPSO algorithm to fault diagnosis demonstrate that it is both feasible and effective. Tests comparing the method proposed in this paper against other fuzzy support vector classifier (FSVC) machines show that it outperforms them.
URI: http://hdl.handle.net/10397/43001
ISSN: 0020-0255 (print)
1872-6291 (online)
DOI: 10.1016/j.ins.2010.08.006
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