Artificial neural networks for vibration based inverse parametric identifications: A review.

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.

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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: Unnamed user with email symplectic@symplectic
Deposited On:25 Jan 2017 09:59
Last Modified:25 Jan 2017 09:59

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