Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16409
Title: A uniformly sampled genetic algorithm with gradient search for system identification
Authors: ZHANG ZHEN
Keywords: Structural health monitoring, genetic algorithm, quasi-random sequence, gradient search, substructural identification
Issue Date: 17-Aug-2009
Source: ZHANG ZHEN (2009-08-17). A uniformly sampled genetic algorithm with gradient search for system identification. ScholarBank@NUS Repository.
Abstract: This study aims to explore the characteristics of structural identification from an optimization perspective. Based on the major findings of peak-shifting, which present the global peak movement due to measurement noise, an enhanced strategy of uniformly sampled genetic algorithm with gradient search is proposed for large-scale system identification. Comparing to recent researches in numerical simulation and experiments, the proposed identification methods achieve more than 90% reduction in computer time while providing better identification accuracy. Further studies in divide-and-conquer strategies of time domain and frequency domain offer a supplementary 96% computer time reduction in evaluating the objective function. More importantly, these strategies produce excellent experiment results in identifying small damage and multiple damages with different magnitudes. Major findings of this study, including some engineering implications of applying the proposed strategies, are highlighted in the conclusion.
URI: http://scholarbank.nus.edu.sg/handle/10635/16409
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZhangZ.pdf6.66 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

395
checked on Dec 2, 2017

Download(s)

387
checked on Dec 2, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.