發(fā)布時(shí)間：2018-01-16 20:26

  本文關(guān)鍵詞：噪聲環(huán)境下的網(wǎng)絡(luò)異常信號(hào)檢測(cè)方法研究　出處：《計(jì)算機(jī)仿真》2016年08期 　論文類型：期刊論文

  更多相關(guān)文章： 濾波器 自適應(yīng) 信號(hào)檢測(cè)

【摘要】：存有噪聲環(huán)境下的網(wǎng)絡(luò)異常信號(hào)檢測(cè),可保證網(wǎng)絡(luò)安全穩(wěn)定的運(yùn)行。網(wǎng)絡(luò)在有噪聲的干擾下容易導(dǎo)致真實(shí)的異常信號(hào)特征與振動(dòng)信號(hào)的頻率極其相似,傳統(tǒng)方法針對(duì)噪聲干擾,無(wú)法提取穩(wěn)定的異常網(wǎng)絡(luò)信號(hào)特征,不能夠準(zhǔn)確完成網(wǎng)絡(luò)信號(hào)特征的分類,使得獲取的網(wǎng)絡(luò)異常信號(hào)檢測(cè)結(jié)果存在較大的偏差。提出梯度自適應(yīng)網(wǎng)絡(luò)異常信號(hào)檢測(cè)方法。以離散的網(wǎng)絡(luò)信號(hào)梯度信息為先驗(yàn)條件進(jìn)行自適應(yīng)去噪,并融合于混沌相空間重構(gòu)理論對(duì)去噪后的離散網(wǎng)絡(luò)信號(hào)進(jìn)行重構(gòu),得到的重構(gòu)時(shí)間序列和ELMAN網(wǎng)絡(luò)算法相結(jié)合,構(gòu)建ELMAN網(wǎng)絡(luò)異常信號(hào)檢測(cè)函數(shù)模型,依據(jù)網(wǎng)絡(luò)異常信號(hào)觀測(cè)的序列值和實(shí)際值間的對(duì)比建立網(wǎng)絡(luò)異常信號(hào)檢測(cè)模型,進(jìn)而完成對(duì)網(wǎng)絡(luò)異常信號(hào)精確檢測(cè)。仿真結(jié)果表明,梯度自適應(yīng)網(wǎng)絡(luò)異常信號(hào)檢測(cè)方法檢測(cè)精確度高。
[Abstract]:The detection of abnormal signals in noisy environment can ensure the safe and stable operation of the network. The characteristics of the real abnormal signals are very similar to the frequency of vibration signals when the network is disturbed by noise. The traditional method can not extract the stable abnormal network signal features and can not accurately complete the classification of the network signal features for noise interference. A gradient adaptive network anomaly signal detection method is proposed. The discrete network signal gradient information is used as a priori condition for adaptive denoising. The chaotic phase space reconstruction theory is used to reconstruct the signal of the de-noised discrete network. The reconstruction time series is combined with the ELMAN network algorithm. The ELMAN network anomaly signal detection function model is constructed, and the network anomaly signal detection model is established according to the comparison between the sequence value and the actual value of the network abnormal signal observation. The simulation results show that the gradient adaptive network anomaly detection method has high accuracy.
【作者單位】： 石家莊鐵道大學(xué);
【分類號(hào)】：TP393.08
【正文快照】： 1引言由于網(wǎng)絡(luò)環(huán)境較為復(fù)雜,致使產(chǎn)生的異常信號(hào)會(huì)與一些毫無(wú)規(guī)律的干擾或噪聲混淆在一起,給網(wǎng)絡(luò)穩(wěn)定的運(yùn)行帶來(lái)了極大的隱患[1-3]。而網(wǎng)絡(luò)異常信號(hào)檢測(cè)技術(shù)是解決這一隱患的有效途徑,引起了很多專家與學(xué)者的重視[4-6]。由于網(wǎng)絡(luò)異常信號(hào)檢測(cè)技術(shù)具有廣闊的發(fā)展空間,因此也成

【參考文獻(xiàn)】

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