Fast detection of impact location using kernel extreme learning machine

UM > Faculty of Science and Technology > DEPARTMENT OF ELECTROMECHANICAL ENGINEERING

Status	已發表Published
	Fast detection of impact location using kernel extreme learning machine
	Fu, H.; Vong, C. M.; Yang, Z. X.
	2013-10-01
Source Publication	Springer
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.
Keyword	Damage location detection Kernel ELM Plate structure
Language	英語English
The Source to Article	PB_Publication
PUB ID	26826
Document Type	Conference paper
Collection	DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Recommended Citation GB/T 7714	Fu, H.,Vong, C. M.,Yang, Z. X.. Fast detection of impact location using kernel extreme learning machine[C], 2013.
APA	Fu, H.., Vong, C. M.., & Yang, Z. X. (2013). Fast detection of impact location using kernel extreme learning machine. Springer.

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