NDLI: A single layer linear feedforward neural network for signal estimation in the wavelet domain

Content Provider	IEEE Xplore Digital Library
Author	Stephanakis, I.M. Kollias, S.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece (Stephanakis, I.M.)
Abstract	A model of adaptive signal estimation in the presence of noise is proposed using the wavelet transform and single layer linear feedforward neural networks. The networks successively estimate the residual of the approximations (details) of the input at different scales from coarse scales (low frequencies) to fine scales by minimizing the error of each channel of the wavelet transform separately. Since learning takes place in subspaces of the signal space one needs to train the networks with fewer weights. The rate of convergence of the learning algorithm in several transform channels is investigated. It is shown that learning in the input space may diverge, whereas convergence is achieved in wavelet spaces. Experimental results are presented which compare the proposed approximation method with adaptive approximation using sigmodal functions and exponentially decaying kernels.
Starting Page	487
Ending Page	490
File Size	286209
Page Count	4
File Format	PDF
ISBN	0780341376
DOI	10.1109/ICDSP.1997.628382
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-07-02
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Feedforward neural networks Estimation Intelligent networks Wavelet domain Convergence Kernel White noise Autocorrelation Discrete wavelet transforms
Content Type	Text
Resource Type	Article

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