Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76655
Title: Forecasting model of dam deformation based on ELMD-LSSVM method
Authors: Wang, F
Zhou, Y 
Zhou, S
Luo, Y
Keywords: Deformation observation
Ensemble local mean decomposition
Least squares support vector machine
Multi-scale
Issue Date: 2016
Publisher: 辽宁工程技术大学
Source: 辽宁工程技术大学学报. 自然科学版 (Journal of Liaoning Technical University. Natural science), 2016, v. 35, no. 12, p. 1475-1479 How to cite?
Journal: 辽宁工程技术大学学报. 自然科学版 (Journal of Liaoning Technical University. Natural science) 
Abstract: For the problem of mode confusion in the implementation of the local mean decomposition, this paper analyzed the theory of LMD and least squares support vector regression, and proposed the forecasting model of dam deformation named ELMD-LSSVM. With the ELMD method, the deformation sequence is decomposed and then each subsequence is forecasted with LSSVM. After reconstruction, the forecasted results are obtained. Experimental results of two models show that the ELMD-LSSVM model is better than other models. It is able to overcome the problem of mode mixing and perform dam deformation in different feature scales simultaneously with those characteristics. So the ELMD-LSSVM model is very suitable to be applied to the dam deformation analysis and prediction of the deformation object.
URI: http://hdl.handle.net/10397/76655
ISSN: 1008-0562
DOI: 10.11956/j.issn.1008-0562.2016.12.017
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