Thresholding wavelet networks for signal classification

Dinesh, K and Pah, N 2003, 'Thresholding wavelet networks for signal classification', International Journal of Wavelets, Multiresolution and Information Processing, vol. 1, no. 3, pp. 243-261.


Document type: Journal Article
Collection: Journal Articles

Title Thresholding wavelet networks for signal classification
Author(s) Dinesh, K
Pah, N
Year 2003
Journal name International Journal of Wavelets, Multiresolution and Information Processing
Volume number 1
Issue number 3
Start page 243
End page 261
Total pages 18
Publisher World Scientific Publishing
Abstract This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does not concentrate necessarily on the high-energy region of the input signals. The network iteratively identifies the suitable wavelet coefficients (scale and translation) that best differentiate the different signals provided during training, irrespective of the ability of these coefficients to represent the signals. The network is not limited to the changes in temporal location of the signal identifiers. This paper also reports the testing of the network using simulated signals.
Subject Information and Computing Sciences not elsewhere classified
Keyword(s) wavelet networks
neural networks
DOI - identifier 10.1142/S0219691303000220
Copyright notice World Scientific Publishing Company
ISSN 0219-6913
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