A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization. (14th July 2014)

• Record Type: Journal Article
• Authors: Xu, Qingsong
• Other Names: Zhang Chengjin Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-07-14
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/906732
• Languages: English
• ISSNs: 1024-123X

Abstract

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.