Time-domain parameter detection of weak signals in the chaotic background

被引：1

作者：

Li, Xiao-Ling ^{[1
]}

Yuan, Ji-Min ^{[3
]}

Yin, Xing ^{[2
]}

Gu, Tian-Xiang ^{[3
]}

机构：

[1] College of Information and Electrical Engineering, Panzhihua University, Panzhihua Sichuan 617000, China

[2] College of Computer, Panzhihua University, Panzhihua Sichuan 617000, China

[3] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2009年 / 38卷 / 04期

关键词：

Phase space methods - Elman neural networks - Time domain analysis - Signal detection - Digital storage;

D O I：

10.3969/j.issn.1001-0548.2009.04.022

中图分类号：

学科分类号：

摘要：

Digital oscilloscope can not measure week signal in chaotic background. A method using Elman neural network is described to achieve signal parameter detection in chaotic background. With the phase space reconstruction theory on time series, the embedded dimension is calculated and used as the in-put dimension of a neural network considered. By adopting the single-step prediction method, the weak signals are detected directly and their waveforms can be gained as well in the chaotic state. Result shows that the method studied in this paper is superior to the existing detection principles. Its feasibility and practicability have been proved by the experiments.

引用

页码：569 / 572

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