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基于增量式雙向主成分分析的機器人感知學習方法研究

王肖鋒 張明路 劉軍

王肖鋒, 張明路, 劉軍. 基于增量式雙向主成分分析的機器人感知學習方法研究[J]. 電子與信息學報, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561
引用本文: 王肖鋒, 張明路, 劉軍. 基于增量式雙向主成分分析的機器人感知學習方法研究[J]. 電子與信息學報, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561
WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561
Citation: WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561

基于增量式雙向主成分分析的機器人感知學習方法研究

doi: 10.11999/JEIT170561
基金項目: 

國家自然科學基金(61503119, 61473113),天津市自然科學基金(15JCYBJC19800, 16JCZDJC30400),天津市智能制造科技重大專項(15ZXZNGX00090)

Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis

Funds: 

The National Natural Science Foundation of China (61503119, 61473113), The Tianjin Natural Science Foundation (15JCYBJC19800, 16JCZDJC30400), The Tianjin Intelligent Manufacturing and Technology Key Project (15ZXZNGX00090)

  • 摘要: 針對直觀協(xié)方差無關增量式主成分分析算法(CCIPCA)需要滿足零均值高斯分布的問題,該文提出含均值差向量更新的泛化CCIPCA算法(GCCIPCA),拓展了算法的適用范圍。其次,針對機器人感知學習存在的在線增量計算及有效數(shù)據(jù)降維等問題,將GCCIPCA的增量思想引入到現(xiàn)有的雙向主成分分析算法(BDPCA),提出基于增量式BDPCA(IBDPCA)的機器人感知學習方法。該方法直接針對圖像矩陣行列方向的類散度矩陣進行迭代估計,具有一定的泛化能力和快速的增量學習能力,提高了實時處理速度。最后,以機器人待抓取物塊作為感知對象進行實驗,結果表明所提算法能夠滿足機器人感知學習的實時處理需求,相比現(xiàn)有的增量式主成分分析算法,在收斂率、分類識別率、計算時間及所需內(nèi)存等性能方面均得到顯著提升。
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出版歷程
  • 收稿日期:  2017-06-09
  • 修回日期:  2017-10-13
  • 刊出日期:  2018-03-19

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