Mohamed Zine EL ABIDINE SKHIRI, Mohamed CHTOUROU
Research Unit on Intelligent Control, Design & Optimization of Complex Systems (ICOS)
Ecole Nationale d’Ingénieurs de Sfax ( ENIS ), BP.W,3038, Sfax, TUNISIA
Abstract: This paper investigates the use of a wavelet denoising unit based on the wavelet mutiresolution analysis on wavelet networks instead of neural networks on which previously reported works have been performed. This full wavelet compound will certainly provide the possibility of building up two denoising analysis models. The most straight forward model is setup by placing the wavelet denoising unit ahead of the network input layer. In other words, the inputs data embedded in a white Gaussian noise of a wavelet network are firstly denoised then fed to the network. An alternative model is also investigated in which the denoising unit will be placed at the output level of the net since a wavelet network is may be considered as a first step smoothing unit. In this analysis version, the noisy signal is fed to the wavelet network and the corresponding output is then applied to the denoising unit.
Keywords: wavelet neural networks, wavelet denoising unit, soft thresholding, hard thresholding.
CITE THIS PAPER AS:
Mohamed Zine EL ABIDINE SKHIRI, Mohamed CHTOUROU, Synthesis of Denoising Wavelet Neural Networks, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (4), pp. 453-464, 2008.