19 April 2013 Multi-objective differential evolution algorithm for stochastic system identification
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Abstract
The last decade has witnessed rapid developments in structural system identification methodologies based on intelligent algorithms, which are formulated as multi-modal optimization problems. However, these deterministic methods more or less ignore uncertainties, such as modeling errors and measurement errors, that are inevitably involved in the system identification problem of civil-engineering structures. A new stochastic structural identification method is proposed that takes into account parametric uncertainties in the parameters of building structures. The proposed method merges the advantages of the multi-objective differential evolution optimization algorithm for the non-domination selection strategy and the probability density evolution method for incorporating parametric uncertainties. The results of simulations on identifying the unknown parameters of a structural system demonstrate the feasibility and effectiveness of the proposed method.
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Jin Zhou, Jin Zhou, Akira Mita, Akira Mita, Rongshuai Li, Rongshuai Li, } "Multi-objective differential evolution algorithm for stochastic system identification", Proc. SPIE 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, 86923G (19 April 2013); doi: 10.1117/12.2006578; https://doi.org/10.1117/12.2006578
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