A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

Qingsong Xu

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

Abstract:

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:906732

DOI: 10.1155/2014/906732

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