BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION
The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain popula...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails?columnId=62615707&Fpath=home&index=0 |
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| _version_ | 1841534058807427072 |
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| author | YE Ling JIANG HongKang ZOU YuQing CHEN HuaPeng WANG LiCheng |
| author_facet | YE Ling JIANG HongKang ZOU YuQing CHEN HuaPeng WANG LiCheng |
| author_sort | YE Ling |
| collection | DOAJ |
| description | The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty. |
| format | Article |
| id | doaj-art-188cf54563a1475f8c0ec18bd326f136 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-188cf54563a1475f8c0ec18bd326f1362025-01-15T02:45:11ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-01-011962615707BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITIONYE LingJIANG HongKangZOU YuQingCHEN HuaPengWANG LiChengThe traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.http://www.jxqd.net.cn/thesisDetails?columnId=62615707&Fpath=home&index=0Model updatingBayesian estimationMarkov Chain Monte CarloPopulation competition |
| spellingShingle | YE Ling JIANG HongKang ZOU YuQing CHEN HuaPeng WANG LiCheng BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION Jixie qiangdu Model updating Bayesian estimation Markov Chain Monte Carlo Population competition |
| title | BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION |
| title_full | BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION |
| title_fullStr | BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION |
| title_full_unstemmed | BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION |
| title_short | BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION |
| title_sort | bayesian finite element model updating based on markov chain population competition |
| topic | Model updating Bayesian estimation Markov Chain Monte Carlo Population competition |
| url | http://www.jxqd.net.cn/thesisDetails?columnId=62615707&Fpath=home&index=0 |
| work_keys_str_mv | AT yeling bayesianfiniteelementmodelupdatingbasedonmarkovchainpopulationcompetition AT jianghongkang bayesianfiniteelementmodelupdatingbasedonmarkovchainpopulationcompetition AT zouyuqing bayesianfiniteelementmodelupdatingbasedonmarkovchainpopulationcompetition AT chenhuapeng bayesianfiniteelementmodelupdatingbasedonmarkovchainpopulationcompetition AT wanglicheng bayesianfiniteelementmodelupdatingbasedonmarkovchainpopulationcompetition |