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基于PCA的BP神经网络分类器 被引量：3

BP Neural Network Classifier Based on Principal Component Analysis
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摘要 由于入侵检测处理的多为高维数据,为了提高入侵检测的效率和准确率,提出了一种基于主成分分析(PCA)的特征提取方法,对数据源进行特征降维,将获得的主成分作为BP神经网络的输入进行数据识别.同时介绍了M atlab中相关函数,并与传统入侵检测方法进行了比较.实验结果表明:基于主成分分析的特征提取方法在简化BP神经网络规模的同时,显著提高了入侵检测识别效果. A feature extraction method using principal component analysis is proposed to improve the efficiency and accuracy of intrusion detection, which always has high - dimensional data. This method reduces data dimensions and views some principal components as the inputs of BP neural network to finish data recognition. Meanwhile, this paper introduces some correlative functions used in MATLAB and makes a comparison with traditional intrusion detection method. The experimental results show that the scale of BP neural network is simplified and recognition effect is improved remarkably.
作者 黄勤 常伟 刘益良 刘衍鹏
机构地区 重庆大学自动化学院 重庆理工大学化学与生物工程学院
出处 《重庆工学院学报（自然科学版）》 2009年第7期89-96,共8页 Journal of Chongqing Institute of Technology
基金 重庆市自然科学基金资助项目(CSTC 2005BA2003)
关键词 入侵检测 主成分分析 特征提取 BP神经网络 MATLAB intrusion detection feature extraction principal component analysis BP neural network Mafia]）
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献10

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二级参考文献15

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共引文献10

同被引文献33

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引证文献3

二级引证文献14

重庆工学院学报（自然科学版）
2009年 第7期

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