中国机械工程

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基于非负矩阵分解的产品结构相似性判断及其应用

徐新胜;王诚;肖颖#br#   

  1. 中国计量学院,杭州,310018
  • 出版日期:2016-04-25 发布日期:2016-04-26
  • 基金资助:
    国家自然科学基金资助项目(51405462,51175486);浙江省科技厅公益性技术应用研究计划资助项目(2013C31132,2014C31117)

Similarity Judgment of Product Structure Based on Non-negative Matrix Factorization and Its Applications

Xu Xinsheng;Wang Cheng;Xiao Ying   

  1. China Jiliang University,Hangzhou,310018
  • Online:2016-04-25 Published:2016-04-26
  • Supported by:

摘要: 为了快速发现可重用产品结构,提出了基于非负矩阵分解的产品结构相似性判断方法。通过将产品结构邻接矩阵转化为邻接向量,构建包含全部结构信息的库矩阵;利用Multiplicative Updates(MU)算法对库矩阵进行非负矩阵分解,实现以低维空间向量描述的产品结构;在此基础上,通过计算低维向量的欧氏距离,可以判断产品结构之间的相似性;最后通过实例对所提出原理和方法进行了验证,结果表明,该方法比目前的相似性判断方法更高效。

关键词: 产品结构, 非负矩阵分解, 相似性, 欧氏距离

Abstract: In order to find reusable product structure promptly, an approach of measuring the similarity among product structures was proposed based on non-negative matrix factorization. A comprehensive matrix which containsed all structures was constructed on the basic of adjacent vectors that were transformed from the adjacent matrices of product structures. The non-negative matrix factorization for the comprehensive matrix was implemented based on Multiplicative Updates(MU) algorithm. Then all product structures might be described in low dimensional space. On the basis of these, the similarity between two product structures could be measured by calculating the Euclidean distance among these low dimensional vectors. Finally, an example was presented to verify the principles and methods mentioned above. The results show that the proposed methodologies are more effective than those of the existing methods.

Key words: product structure, non-negative matrix factorization, similarity, Euclidean distance

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