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A hybrid immune model for unsupervised structural damage pattern recognition
Chen B(陈波); Zang CZ(臧传治)
Department工业信息学研究室
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
ISSN0957-4174
2011
Volume38Issue:3Pages:1650-1658
Indexed BySCI ; EI
EI Accession number20104513367266
WOS IDWOS:000284863200041
Contribution Rank1
KeywordStructural Health Monitoring Unsupervised Structural Damage Pattern Recognition Fuzzy Clustering Artificial Immune Pattern Recognition
AbstractThis paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve the quality of memory cells, the artificial immune pattern recognition method based on immune learning mechanisms is employed to evolve memory cells. The presented hybrid immune model (combined with fuzzy clustering and the artificial immune pattern recognition) has been tested 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 test results show the feasibility of using the hybrid AIPR (HAIPR) method for the unsupervised structural damage pattern recognition. (C) 2010 Elsevier Ltd. All rights reserved.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS KeywordSYSTEMS
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/7058
Collection工业信息学研究室
Affiliation1.Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Drive, Houghton, MI 49931, United States
2.Department of Electrical and Computer Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, United States
3.Shenyang Institute of Automation, Chinese Academy of Science, Nanta Street 114, Shenyang, Liaoning 110016, China
Recommended Citation
GB/T 7714
Chen B,Zang CZ. A hybrid immune model for unsupervised structural damage pattern recognition[J]. EXPERT SYSTEMS WITH APPLICATIONS,2011,38(3):1650-1658.
APA Chen B,&Zang CZ.(2011).A hybrid immune model for unsupervised structural damage pattern recognition.EXPERT SYSTEMS WITH APPLICATIONS,38(3),1650-1658.
MLA Chen B,et al."A hybrid immune model for unsupervised structural damage pattern recognition".EXPERT SYSTEMS WITH APPLICATIONS 38.3(2011):1650-1658.
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