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Title: A uniformly sampled genetic algorithm with gradient search for system identification
Keywords: Structural health monitoring, genetic algorithm, quasi-random sequence, gradient search, substructural identification
Issue Date: 17-Aug-2009
Citation: 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.
Appears in Collections:Ph.D Theses (Open)

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