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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/10120

Title: A Nonlinear Time-Varying Channel Equalizer Using Self-Organizing Wavelet Neural Networks
Authors: Lin, Cheng-Jian;Shih, Chuan-Chan;Chen, Po-Yueh
Contributors: 資訊工程學系
Keywords: Additive white Gaussian noise;Back-propagation;Equalizer;Time-varying channel;Wavelet neural network
Date: 2004-07
Issue Date: 2012-05-03T09:14:24Z
Publisher: IEEE, Piscataway NJ, ETATS-UNIS
Abstract: This paper describes the self-organizing wavelet neural network (SOWNN) for nonlinear time-varying channel equalizers. The SOWNN model has a four-layer structure which is comprised of an input layer, a wavelet layer, a product layer and an output layer. The derivative online learning algorithm involves two kinds of learning. The structure learning is performed to determine the network structure and the parameter learning is to adjust the shape of the wavelet bases and the connection weights of a SOWNN. The proposed equalizer is enhanced in order to handle the highly nonlinear functionality. Computer simulation results show that the bit error rate of the SOWNN equalizer is very close to that of the optimal equalizer.
Relation: 2004 IEEE International Joint Conference on Neural Networks , July 25-29, 2004: 2089-2094
Appears in Collections:[資訊工程學系] 會議論文

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