A competitive wavelet network for signal clustering

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Roberto K H Galvão, Takashi Yoneyama

Abstract

This correspondence proposes a novel signal clustering method based on the unsupervised training of a wavelet network. The synaptic weights are parameterized by wavelet basis functions, which are adjusted by a competitive algorithm that makes use of the neighborhood concept proposed by Kohonen. The robustness of the wavelet network with respect to noise is illustrated in a simulated problem, in which dynamic systems are grouped on the basis of their step responses. An example involving clustering of electrocardiographic signals taken from the MIT-BIH database is also presented. In this case, the ability of the proposed network to perform clustering at successive resolution levels is illustrated. The possibility of interpreting the information encoded in the network at the end of training is also discussed.

References

Feb 14, 1998·IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society·S BarroE Sánchez
Oct 15, 1998·IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society·R M Gulrajani
Feb 2, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·C R de SousaR H Galvao
Feb 7, 2008·IEEE Transactions on Neural Networks·J P SumL W Chan
Jan 1, 1992·IEEE Transactions on Neural Networks·Q Zhang, A Benveniste

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Citations

Dec 5, 2008·IEEE Transactions on Neural Networks·Hong Pan, Liang-Zheng Xia
Mar 20, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Zhi LiuYun Zhang

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