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题名: Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks
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: SmaRT Sensor Phenomena, Technology, Networks, and Systems 2009
会议日期: March 9-11, 2009
会议地点: San Diego, CA, United states
会议主办者: SPIE
会议录: Proc. SPIE
会议录出版者: SPIE
会议录出版地: BELLINGHAM
出版日期: 2009
页码: 72930K
收录类别: EI
关键词: Artificial immune pattern recognition ; structural health monitoring ; structure damage classification
摘要: This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.
语种: 英语
产权排序: 2
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8334
Appears in Collections:工业信息学研究室_会议论文

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Recommended Citation:
陈波; 臧传治.Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks.见:SPIE.Proc. SPIE,BELLINGHAM,2009,72930K
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