A Wavelet neural network for detection of signals in communications

被引:5
|
作者
Gomez-Sanchez, R [1 ]
Andina, D [1 ]
机构
[1] Univ Politecn Madrid, Dept Senales Sistemas & Radiocommun, Madrid 28044, Spain
来源
WAVELET APPLICATIONS V | 1998年 / 3391卷
关键词
detection; communications; wavelets; neural networks; a priori probabilities; nonlinear models;
D O I
10.1117/12.304876
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Our objective is the design and simulation of an efficient system for detection of signals in communications in terms of speed and computational. complexity. The proposed scheme takes advantage of two powerful frameworks in signal processing: Wavelets and Neural Networks. The decision system will take a decision based on the computation of the a priori probabilities of the input signal. For the estimation of such probability density functions, a Wavelet Neural Network (WNN) has been chosen. The election has arosen under the following considerations: (a) neural networks have been established as a general approximation tool for fitting nonlinear models from input/output data and (b) the increasing popularity of the wavelet decomposition as a powerful tool for approximation. The integration of the above factors leads to the wavelet neural network concept. This network preserve the universal approximation property of wavelet series, with the advantage of the speed and efficient computation of a neural network architecture. The topology and learning algorithm of the network will provide an efficient approximation to the required probability density functions.
引用
收藏
页码:265 / 274
页数:10
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