Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks
文献类型：会议论文
| 作者 | Chen B(陈波); Zang CZ(臧传治)
|
| 出版日期 | 2009 |
| 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 |
| 关键词 | Artificial immune pattern recognition structural health monitoring structure damage classification |
| 页码 | 72930K |
| 中文摘要 | 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. |
| 收录类别 | EI |
| 产权排序 | 2 |
| 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 |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/8334]  |
| 专题 | 沈阳自动化研究所_工业信息学研究室 |
| 推荐引用方式 GB/T 7714 | Chen B,Zang CZ. Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks[C]. 见:SmaRT Sensor Phenomena, Technology, Networks, and Systems 2009. San Diego, CA, United states. March 9-11, 2009. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。