NDLI: The application of classification wavelet networks to the recognition of transient signals

Content Provider	IEEE Xplore Digital Library
Author	Hoffman, A.J. Tollig, C.J.A.
Copyright Year	1999
Description	Author affiliation: Sch. for Electr. & Electron. Eng., Potchefstroom Univ. for CHE, South Africa (Hoffman, A.J.)
Abstract	It has been demonstrated before that wavelets and neural networks can be successfully combined for the classification of transient signals. In the so-called classification wavelet network (CWN) the wavelet transform is applied in the first hidden layer of the network to extract compact features from input signals. This is followed by further layers to perform classification of the wavelet features. Since the definition of features is crucial to the success of a neural classifier, it may be advantageous if the definition of the features could be optimised. This paper demonstrates that the definition of wavelet-based features can be optimised as part of the training process of the CWN. The parameters that are optimised are the dilations and translations of the wavelet components. The mutual information criterion is employed initially to select a relatively small set of wavelets from the time-frequency grid. It is then demonstrated that the initial performance of the CWN can be improved by optimising the definition of the wavelet-based features. This technique is evaluated by application to the classification of seismic buffers.
Starting Page	407
Ending Page	410
File Size	349991
Page Count	4
File Format	PDF
ISBN	0780355466
DOI	10.1109/AFRCON.1999.820883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-09-28
Publisher Place	South Africa
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Continuous wavelet transforms Neural networks Mutual information Time frequency analysis Feature extraction Channel hot electron injection Africa Displays Signal representations
Content Type	Text
Resource Type	Article

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