Goh, Lyn Dee and Dee, Dee and Bakhary, Norhisham and Ahmad, Baderul Hisham (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. In: 2011 International Conference On Structures And Building Materials (ICSBM 2011).
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Official URL: http://dx.doi.org/10.4028/www.scientific.net/AMR.1...
Abstract
This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | artificial neural network |
| Divisions: | Civil Engineering |
| ID Code: | 45474 |
| Deposited By: | Haliza Zainal |
| Deposited On: | 10 Jun 2015 03:00 |
| Last Modified: | 20 Sep 2017 00:50 |
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