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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.15 No.3
- 발행연도
- 2015.1
- 수록면
- 735 - 749 (15page)
이용수
초록· 키워드
오류제보하기
The major difficulty of using Bayesian probabilistic inference for system identification is toobtain the posterior probability density of parameters conditioned by the measured response. The posteriordensity of structural parameters indicates how plausible each model is when considering the uncertainty ofprediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posteriorinference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting(DREAM) algorithm boasts a population-based mechanism, which runs multiple different Markov chainssimultaneously, and a global optimum exploration ability. This paper proposes an improved differentialevolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density ofstructural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of theadaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robustsolutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters withlimited output data and noise polluted measurements.
목차
등록된 정보가 없습니다.
참고문헌 (19)
참고문헌 신청
Beck, J. L. / 2010 / Bayesian system identification based on probability logic / J. Struct. Control Health Monit 17(7):825~847
Beck, J.L. / 2002 / Bayesian updating of structural models and reliability using Markov-Chain Monte-Carlo simulation / J. Eng. Mech. -ASCE 128(4):380~391
Beck, J.L. / 2004 / Model selection using response measurements : A Bayesian probabilistic approach / J. Eng. Mech. -ASCE 130(2):192~203
Cheung, S.H. / 2009 / Bayesian model updating using Hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters / J. Eng. Mech. -ASCE 135(4):243~255
Chib, S. / 1995 / Understanding the Metropolis-Hasting algorithm / Am. Statist. Association 49(4):327~335
Beck, J.L. / 2002 / Bayesian updating of structural models and reliability using Markov-Chain Monte-Carlo simulation / J. Eng. Mech. -ASCE 128(4):380~391
Beck, J.L. / 2004 / Model selection using response measurements : A Bayesian probabilistic approach / J. Eng. Mech. -ASCE 130(2):192~203
Cheung, S.H. / 2009 / Bayesian model updating using Hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters / J. Eng. Mech. -ASCE 135(4):243~255
Chib, S. / 1995 / Understanding the Metropolis-Hasting algorithm / Am. Statist. Association 49(4):327~335
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