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Emergent damage pattern recognition using immune network theory
Chen B(陈波); Zang CZ(臧传治)
Department工业信息学研究室
Source PublicationSMART STRUCTURES AND SYSTEMS
ISSN1738-1584
2011
Volume8Issue:1Pages:69-92
Indexed BySCI
WOS IDWOS:000293367200006
Contribution Rank2
Funding OrganizationThis research is supported by the National Science Foundation under Grant No. 1049294 and Michigan Tech Research Excellence Fund. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions. The authors would like to thank Wenjia Liu for his contributions to the simulation work described in this article.
KeywordEmergent Pattern Recognition Immune Network Theory Hierarchical Clustering Artificial Immune Systems
AbstractThis paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectEngineering, Civil ; Engineering, Mechanical ; Instruments & Instrumentation
WOS KeywordMONITORING-SYSTEM ; CLUSTERS ; NUMBER ; ALGORITHM ; SELECTION
WOS Research AreaEngineering ; Instruments & Instrumentation
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/7079
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, USA
2.Department of Electrical and Computer Engineering, Michigan Technological University, USA
3.Shenyang Institute of Automation, Chinese Academy of Science, Nanta Street 114, Shenyang, Liaoning, P.R. China, 110016
Recommended Citation
GB/T 7714
Chen B,Zang CZ. Emergent damage pattern recognition using immune network theory[J]. SMART STRUCTURES AND SYSTEMS,2011,8(1):69-92.
APA Chen B,&Zang CZ.(2011).Emergent damage pattern recognition using immune network theory.SMART STRUCTURES AND SYSTEMS,8(1),69-92.
MLA Chen B,et al."Emergent damage pattern recognition using immune network theory".SMART STRUCTURES AND SYSTEMS 8.1(2011):69-92.
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