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基于流形学习的局部保持PCA算法在故障检测中的应用
  • ISSN号：1001-0920
  • 期刊名称：《控制与决策》
  • 时间：0
  • 分类：TP273[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
  • 作者机构：[1]东北大学信息科学与工程学院,沈阳110819, [2]渤海大学工学院,辽宁锦州121000
  • 相关基金：国家自然科学基金重点项H（61273164,61034005）;国家高技术研究发展计划项W（2012AA040104）;中央高校基本科研业务费项目（N100104102,N120504002）.
作者： 王健[1,2], 冯健[1], 韩志艳[2]
关键词： 主元分析, 局部保持, 故障检测, 流形学习, principal component analysis（PCA）, locality preserving projections（LPP）, fault detection, manifold learning
中文摘要：

提出一种新的基于流形学习的数据降维及特征提取方法：局部保持PCA算法（LPPCA）．通过在PCA的优化目标中融入流形学习的思想，不仅使投影得到的低维空间和原始样本空间具有相似的全局结构，并且保持了相似的局部近邻结构，克服了传统PCA方法只关注全局结构特征而忽略局部流形特征的缺陷，同时给出了LPPCA在故障检测中的应用方法．S-Curve和Swiss-roll曲面数值仿真和TE过程仿真验证了算法的有效性和优越性．

英文摘要：

A novel dimensionality reduction and feature extraction method based on manifold learning, locally preserving principal component analysis（LPPCA） is proposed. In order to overcome the defects that the traditional PCA can only keep the structure in global and can not maintain the manifold structure in local, the idea of locality preserving is incorporated into the optimization goals of the PCA. The fault detection based on LPPCA is researched. The validity and superiority of the LPPCA are verified by the S-Curve numerical simulation, Swiss-roll surface numerical simulation and TE process simulation.

同期刊论文项目
基于数据驱动的复杂控制过程故障诊断和监控理论与关键技术研究
期刊论文 279 会议论文 8 获奖 34 专利 16 著作 7
面向不平衡样本的流形学习故障诊断方法
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期刊信息
  • 《控制与决策》
  • 北大核心期刊（2011版）
  • 主管单位:中华人民共和国教育部
  • 主办单位:东北大学
  • 主编：张嗣瀛 王福利
  • 地址：沈阳市东北大学125信箱
  • 邮编：110004
  • 邮箱：kzyjc@mail.neu.edu.cn
  • 电话：024-83687766
  • 国际标准刊号：ISSN：1001-0920
  • 国内统一刊号：ISSN：21-1124/TP
  • 邮发代号:8-51
  • 获奖情况:
  • 1997年被评为辽宁省优秀编辑部,1999年期刊影响因子在信息与系统类期刊中排名第二位
  • 国内外数据库收录:
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  • 被引量:32961