UM  > Faculty of Science and Technology  > DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Structure impact localization using emerging artificial intelligence algorithms
Xu Q.
2015-10-07
Source PublicationEmerging Design Solutions in Structural Health Monitoring Systems
PublisherIGI-GLOBAL
Pages103-123
Abstract

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

DOI10.4018/978-1-4666-8490-4.ch006
URLView the original
Language英语
ISBN9781466684911;1466684909;9781466684904;
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Citation statistics
Document TypeBook chapter
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xu Q.. Structure impact localization using emerging artificial intelligence algorithms:IGI-GLOBAL,2015:103-123.
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