Smart Structures and Systems

  • 제8권1호
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  • Pages.69-92
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  • 2011
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Emergent damage pattern recognition using immune network theory

  • Chen, Bo (Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University) ;
  • Zang, Chuanzhi (Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University)
  • 투고 : 2010.04.15
  • 심사 : 2010.11.28
  • 발행 : 2011.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

This 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.

키워드

과제정보

연구 과제 주관 기관 : National Science Foundation

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피인용 문헌

  1. A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring vol.2015, 2015, https://doi.org/10.1155/2015/648097