本文關(guān)鍵詞： 局部均值分解 BP神經(jīng)網(wǎng)絡(luò) 仿真信號 端部效應(yīng)　出處：《電子技術(shù)應(yīng)用》2017年05期 　論文類型：期刊論文

【摘要】：詳細闡述了局部均值分解(LMD)信號處理方法,該方法非常適合處理非平穩(wěn)信號,可其端部效應(yīng)嚴重制約了其進一步應(yīng)用推廣。鏡像延拓是局域分解端部效應(yīng)處理的基本途徑,需要鏡像面放置在局部極值點處,而實際信號有時難以滿足這個條件,可能導致信號分解結(jié)果嚴重失真現(xiàn)象。為此,提出了一種基于傳統(tǒng)鏡像延拓與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合進行信號預測以改進LMD端部效應(yīng)消除效果的新方法。通過BP神經(jīng)網(wǎng)絡(luò)模型預測原始信號端點之外的數(shù)據(jù)點,由此捕捉到端點之外的一個或者多個極值點,再用鏡像技術(shù)形成閉合處理,從而抑制端部效應(yīng)。仿真信號的應(yīng)用實例表明,所提方法可以有效抑制LMD端部效應(yīng)。
[Abstract]:The method of local mean decomposition (LMD) signal processing is described in detail. This method is very suitable for dealing with non-stationary signals, but its end effect seriously restricts its further application and popularization. Image continuation is the basic way to deal with local decomposition end effects. The image plane needs to be placed at the local extremum, and the actual signal is sometimes difficult to satisfy this condition, which may lead to serious distortion of the signal decomposition result. A new method of signal prediction based on the combination of traditional image continuation and BP neural network is proposed to improve the effect of eliminating the end effect of LMD. The BP neural network model is used to predict the data points outside the original signal endpoint. One or more extreme points outside the endpoint are captured, and then closed processing is formed by mirror image technique to suppress the end effect. The application of the simulation signal shows that the proposed method can effectively suppress the end effect of LMD.
【作者單位】： 江蘇第二師范學院數(shù)學與信息技術(shù)學院;江蘇第二師范學院信息化建設(shè)與管理辦公室;南京南瑞集團信息通信技術(shù)分公司;
【基金】：國家自然科學基金(61272506) 國家科技支撐計劃課題(2007BAB18B01)
【分類號】：TN911.7;TP183
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本文編號：1527686