KEYWORDS: Time series analysis, Particle swarm optimization, System identification, Binary data, Interference (communication), Evolutionary algorithms, Systems modeling, Data analysis, Optimization (mathematics), Performance modeling
A novel symbolization strategy, "dynamic" strategy, of symbolization state series analysis (STSA) is employed in symbolization-based differential evolution strategy (SDES) to alleviate the effects of harmful noise. Procedure of "dynamic" strategy is described, effect of parameters in "dynamic" is verified, cases of partial output are considered. Performance of the proposed methodology was numerically compared with other symbolization strategies. Particle swarm optimization (PSO) and differential evolution (DE) on raw acceleration data are used as comparison to show the good noise immunity of our proposed methodology. These simulations revealed that our proposed methodology is a powerful tool for identifying the unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.
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.
Cloud model is a new mathematical representation of linguistic concepts, which shows potentials for uncertainty
mediating between the concept of a fuzzy set and that of a probability distribution. This paper utilizes cloud model theory
as an uncertainty analyzing tool for noise-polluted signals, which formulates membership degree functions of residual
errors that quantify the difference between the prediction from simulated model and the actual measured time history at
each time interval. With membership degree functions a multi-objective optimization strategy is proposed, which
minimizes multiple error terms simultaneously. Its non-domination-based convergence provides a stronger constraint that
enables robust identification of damages with lower damage negative false. Simulation results of a structural system
under noise polluted signals are presented to demonstrate the effectiveness of the proposed method.
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