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Smart Structures and Systems
  Volume 15, Number 3, March 2015 , pages 735-749

Posterior density estimation for structural parameters using improved differential evolution adaptive Metropolis algorithm
Jin Zhou, Akira Mita and Liu Mei

    The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which runs multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.
Key Words
    structural identification; differential evolution; adaptive metropolis-hastings; Markov chain Monte Carlo; structural parameter estimation; Bayesian posterior probability density
Jin Zhou, Akira Mita and Liu Mei: Department of System Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

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