Hossain, M.S., Ong, Z.C., Ismail, Z., Noroozi, S. and Khoo, S.Y., 2017. Artificial neural networks for vibration based inverse parametric identifications: A review. Applied Soft Computing, 52 (March), 203 - 219.
Full text available as:
|
PDF
10 1016@j asoc 2016 12 014. noroozi.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. 1MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.1016/j.asoc.2016.12.014
Abstract
Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes.
Item Type: | Article |
---|---|
ISSN: | 1568-4946 |
Uncontrolled Keywords: | Artificial neural networks; Inverse problems; Parametric identification; Vibration |
Group: | Faculty of Science & Technology |
ID Code: | 26552 |
Deposited By: | Symplectic RT2 |
Deposited On: | 25 Jan 2017 09:59 |
Last Modified: | 14 Mar 2022 14:02 |
Downloads
Downloads per month over past year
Repository Staff Only - |