A Novel Machine Learning Based Method of Combined Dynamic Environment Prediction

Wentao Mao, Guirong Yan and Longlei Dong

Mathematical Problems in Engineering, 2013, vol. 2013, 1-15

Abstract:

In practical engineerings, structures are often excited by different kinds of loads at the same time. How to effectively analyze and simulate this kind of dynamic environment of structure, named combined dynamic environment, is one of the key issues. In this paper, a novel prediction method of combined dynamic environment is proposed from the perspective of data analysis. First, the existence of dynamic similarity between vibration responses of the same structure under different boundary conditions is theoretically proven. It is further proven that this similarity can be established by a multiple-input multiple-output regression model. Second, two machine learning algorithms, multiple-dimensional support vector machine and extreme learning machine, are introduced to establish this model. To test the effectiveness of this method, shock and stochastic white noise excitations are acted on a cylindrical shell with two clamps to simulate different dynamic environments. The prediction errors on various measuring points are all less than ±3 dB, which shows that the proposed method can predict the structural vibration response under one boundary condition by means of the response under another condition in terms of precision and numerical stability.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2013/141849.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2013/141849.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:141849

DOI: 10.1155/2013/141849

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().