Paper Title:
A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection
  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.

  Info
Periodical
Advanced Materials Research (Volumes 163-167)
Edited by
Lijuan Li
Pages
2756-2760
DOI
10.4028/www.scientific.net/AMR.163-167.2756
Citation
G. Lyn Dee, N. Bakhary, A. Abdul Rahman, B. Hisham Ahmad, "A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection", Advanced Materials Research, Vols. 163-167, pp. 2756-2760, 2011
Online since
December 2010
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Price
$32.00
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