A hybrid immune model for unsupervised structural damage pattern recognition
Document Type
Article
Publication Date
3-1-2011
Abstract
This 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. © 2010 Elsevier Ltd. All rights reserved.
Publication Title
Expert Systems with Applications
Recommended Citation
Chen, B.,
&
Zang, C.
(2011).
A hybrid immune model for unsupervised structural damage pattern recognition.
Expert Systems with Applications,
38(3), 1650-1658.
http://doi.org/10.1016/j.eswa.2010.07.087
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/6393