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Fast detection of impact location using kernel extreme learning machine

  • Extreme Learning Machine and Applications
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Abstract

Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.

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Acknowledgments

This work is co-supported by FDCT Macau SAR, Grant No. FDCT/075/2013/A, and the research grants of University of Macau, Grant Nos. MYRG075(Y1-L2)-FST13-VCM and MYRG075(Y2-L2)-FST12-VCM. The authors would also like to express their sincere gratitude to Dr. Q. S. Xu and Mr. Kehn Wong for offering the experiment devices and technical support.

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Correspondence to Chi-Man Vong.

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Fu, H., Vong, CM., Wong, PK. et al. Fast detection of impact location using kernel extreme learning machine. Neural Comput & Applic 27, 121–130 (2016). https://doi.org/10.1007/s00521-014-1568-2

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  • DOI: https://doi.org/10.1007/s00521-014-1568-2

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