基于双提升小波变换的混沌信号自适应降噪方法

Yunxia Liu, X. Liao
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引用次数: 3

摘要

基于混沌信号与高斯噪声的不同特征,提出了一种双提升小波变换自适应降噪方法。该方法分为两个主要步骤:逼近信号的估计和细节系数的自适应选择。前者采用奇异谱分析方法处理,后者则结合神经网络中的梯度体面算法进行分析。分别将Lorenz模型产生的混沌信号和观测到的太阳黑子月序列进行仿真分析,实验结果表明,所提方法的性能优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Noise Reduction Method for Chaotic Signals Using Dual-Lifting Wavelet Transform
Based on different features between chaotic signals and Gaussian noises, an adaptive noise reduction method is proposed using dual-lifting wavelet transform. The proposed method has two major steps: the estimation of approximation signals and the adaptive choice of detail coefficients. The former is handled by singular spectrum analysis, whereas the latter is analyzed combining with gradient decent algorithm in neural networks. The chaotic signals generated by Lorenz model as well as the observed monthly series of sunspots are respectively applied for simulation analysis, the experimental results show that the performance of the proposed method is superior to that of other methods.
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