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

Abstract EN

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.