摘要
为了有效地利用结构健康监测系统冗余、互补的信息进行结构健康状况评估,该文提出了一种将粗集、数据融合和概率神经网络(PNN)有机地结合在一起的损伤识别新方法。它先用粗集进行属性约简来降低数据的空间维数,然后运用PNN进行融合计算来处理冗余、不确定信息,最后进行融合决策和损伤识别。在粗集属性约简过程中,提出了运用K-均值聚类的方法进行数据离散的处理方法。为了验证所提方法的有效性,对2个数值算例的多种损伤模式进行了识别,并与没有经过粗集处理的PNN损伤识别方法进行了比较。研究发现,该文所提方法不仅可以降低数据的空间维数,而且具有很高的损伤识别精度。
In order to make full use of the redundant and complementary information and to assess the structural health states from a structural health monitoring system, a new damage identification method is proposed by integrating with rough set, data fusion and probabilistic neural network （PNN）. In this method, rough set is used to reduce attributes so as to decrease spatial dimensions of data firstly, then PNN is utilized to fuse redundant and uncertain information and fusion decision-making and damage identification results are made. It is noteworthy that K-means clustering was employed to discrete data during the attributes reduction. To validate the efficiency of the proposed method, multi-damage patterns from two numerical examples were identified finally, and a comparison was made between the proposed method and a PNN classifier without data processing by rough set. The results show that the proposed method can not only reduce spatial dimension of data, but also have good damage identification accuracy.
出处
《工程力学》
EI
CSCD
北大核心
2009年第4期207-213,共7页
Engineering Mechanics
基金
国家自然科学基金项目(50408033
50878057)
辽宁优秀人才计划项目(RC-05-16)
福建高校优秀人才计划项目(2007)
教育部重点和福建教育厅重点项目(208064)
关键词
粗集:损伤识别
属性约简
概率神经网络
数据融合
rough set
damage identification
attributes reduction
probabilistic neural network
data fusion
