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题名: Unsupervised Structure Damage Classification Based on the Data Clustering and Artificial Immune Pattern Recognition
作者: Chen B(陈波) ; Zang CZ(臧传治)
作者部门: 工业信息学研究室
会议名称: 8th International Conference on Artificial Immune Systems
会议日期: August 9-12, 2009
会议地点: York, ENGLAND
会议录: ARTIFICIAL IMMUNE SYSTEMS, PROCEEDINGS
会议录出版者: SPRINGER-VERLAG
会议录出版地: BERLIN
出版日期: 2009
页码: 206-219
收录类别: CPCI(ISTP) ; EI
ISSN号: 0302-9743
ISBN号: 978-3-642-03245-5
摘要: This paper presents an unsupervised structure damage classification algorithm based on the data clustering technique and the artificial immune pattern recognition. The presented method uses time series measurement of a structure's dynamic response to extract damage-sensitive features for the structure damage classification. The Data Clustering (DC) technique is employed to cluster training data to a specified number of clusters and generate the initial memory cell set. The Artificial Immune Pattern Recognition (AIPR) algorithms are integrated with the data clustering algorithms to provide a mechanism for the evolution of memory cells. The combined DC-AIPR method has been tested using a benchmark structure. The test results show the feasibility of using the DC-AIPR method for the unsupervised structure damage classification.
产权排序: 2
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8429
Appears in Collections:工业信息学研究室_会议论文

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