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

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.