基于改進(jìn)FastICA算法的入侵檢測樣本數(shù)據(jù)優(yōu)化方法
發(fā)布時間:2018-11-18 14:31
【摘要】:為更好實現(xiàn)對入侵檢測樣本數(shù)據(jù)的優(yōu)化處理,提出了一種改進(jìn)的快速獨立成分分析(Fast ICA)算法,采用基于加權(quán)相關(guān)系數(shù)進(jìn)行白化處理以減少信息損失,并優(yōu)化牛頓迭代法使其滿足三階收斂。對算法進(jìn)行了細(xì)致描述,分析了算法的時間復(fù)雜度。實驗結(jié)果表明,該方法可有效減少數(shù)據(jù)信息損失,具有迭代次數(shù)少、收斂速度快等優(yōu)點,可有效提高入侵檢測樣本數(shù)據(jù)的優(yōu)化效率。
[Abstract]:In order to achieve the optimal processing of intrusion detection sample data, an improved fast independent component analysis (Fast ICA) algorithm is proposed, which is whitened based on weighted correlation coefficient to reduce information loss. The Newton iteration method is optimized to satisfy the third order convergence. The algorithm is described in detail and its time complexity is analyzed. Experimental results show that this method can effectively reduce the loss of data information, has the advantages of less iteration times and faster convergence speed, and can effectively improve the optimization efficiency of intrusion detection sample data.
【作者單位】: 北京交通大學(xué)計算機(jī)與信息技術(shù)學(xué)院;
【基金】:北京高校青年英才計劃基金資助項目(No.YETP0548) 中央高;究蒲袠I(yè)務(wù)費基金資助項目(No.2014JBM030) 國家自然科學(xué)基金資助項目(No.61102105)~~
【分類號】:TP393.08
本文編號:2340299
[Abstract]:In order to achieve the optimal processing of intrusion detection sample data, an improved fast independent component analysis (Fast ICA) algorithm is proposed, which is whitened based on weighted correlation coefficient to reduce information loss. The Newton iteration method is optimized to satisfy the third order convergence. The algorithm is described in detail and its time complexity is analyzed. Experimental results show that this method can effectively reduce the loss of data information, has the advantages of less iteration times and faster convergence speed, and can effectively improve the optimization efficiency of intrusion detection sample data.
【作者單位】: 北京交通大學(xué)計算機(jī)與信息技術(shù)學(xué)院;
【基金】:北京高校青年英才計劃基金資助項目(No.YETP0548) 中央高;究蒲袠I(yè)務(wù)費基金資助項目(No.2014JBM030) 國家自然科學(xué)基金資助項目(No.61102105)~~
【分類號】:TP393.08
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