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摘 要:經(jīng)驗(yàn)?zāi)Ｊ椒纸猓‥MD）是一種自適應(yīng)的分解算法. 現(xiàn)實(shí)中采集到的信號(hào)總或多或少帶有一些噪聲成分. 通過對(duì)信號(hào)的經(jīng)驗(yàn)?zāi)Ｊ椒纸膺M(jìn)行分析，發(fā)現(xiàn)信號(hào)中包含的噪聲對(duì)分解結(jié)果影響較大. 本文對(duì)于幅值較大的噪聲，先利用小波變換的降噪功能，對(duì)信號(hào)進(jìn)行小波閾值降噪，再和EMD分解相結(jié)合，能更準(zhǔn)確的得到分析結(jié)果.- 關(guān)鍵詞：經(jīng)驗(yàn)?zāi)Ｊ椒纸猓恍〔ń翟?；閾值降?/p>
Abstract:The empirical mode decomposition(EMD) is an adaptive method for signal processing. From the decomposition results of some familiar signals using EMD, it is found that the noise in the signal affected the results greatly. For amplitude larger noise, firstly, the noise of the signal is reduced in the form of wavelet value threshold by using wavelet transform noise reduction function, combining with EMD. In this way, more accurate results of analysis could be obtained. Keywords: empirical mode decomposition; wavelet noise reduction; value threshold de-noising
目錄 第一章 引言-1 第二章 經(jīng)驗(yàn)?zāi)Ｊ椒纸?3 2.1 經(jīng)驗(yàn)?zāi)Ｊ椒纸庠?3 2.2 EMD實(shí)例-6 第三章 小波降噪-8 第四章 實(shí)驗(yàn)信號(hào)加噪分析-10 第五章 結(jié)論-16 參考文獻(xiàn)-17 致 謝-18 |
