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A comparison of artificial neural network learning algorithms for vibration-based damage detection

Goh, Lyn Dee and Bakhary, Norhisham and Abdul Rahman, Azlan and Ahmad, Baderul Hisham (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. Advanced Materials Research, 163-16 . pp. 2756-2760. ISSN 1022-6680

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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:Article
Uncontrolled Keywords:artificial neural network
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Civil Engineering
ID Code:44681
Deposited By: Haliza Zainal
Deposited On:21 Apr 2015 03:31
Last Modified:29 Aug 2017 06:46

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