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• W2000647419 abstract "Cloud model is a new mathematical representation of linguistic concepts, which shows potentials for uncertaintymediating between the concept of a fuzzy set and that of a probability distribution. This paper utilizes cloud model theoryas an uncertainty analyzing tool for noise-polluted signals, which formulates membership degree functions of residualerrors that quantify the difference between the prediction from simulated model and the actual measured time history ateach time interval. With membership degree functions a multi-objective optimization strategy is proposed, whichminimizes multiple error terms simultaneously. Its non-domination-based convergence provides a stronger constraint thatenables robust identification of damages with lower damage negative false. Simulation results of a structural systemunder noise polluted signals are presented to demonstrate the effectiveness of the proposed method." @default.
• W2000647419 created "2016-06-24" @default.
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• W2000647419 creator A5017604817 @default.
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• W2000647419 date "2012-04-26" @default.
• W2000647419 modified "2023-09-23" @default.
• W2000647419 title "Multi-objective optimization strategies for damage detection using cloud model theory" @default.
• W2000647419 doi "https://doi.org/10.1117/12.914231" @default.
• W2000647419 hasPublicationYear "2012" @default.
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