کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
697487 890372 2008 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains
چکیده انگلیسی
We consider the smoothing problem of estimating a sequence of state vectors given a nonlinear state space model with additive white Gaussian noise, and measurements of the system output. The system output may also be nonlinearly related to the system state. Often, obtaining the minimum variance state estimates conditioned on output data is not analytically intractable. To tackle this difficulty, a Markov chain Monte Carlo technique is presented. The proposal density for this method efficiently draws samples from the Laplace approximation of the posterior distribution of the state sequence given the measurement sequence. This proposal density is combined with the Metropolis-Hastings algorithm to generate realizations of the state sequence that converges to the proper posterior distribution. The minimum variance estimate and confidence intervals are approximated using these realizations. Simulations of a fed-batch bioreactor model are used to demonstrate that the proposed method can obtain significantly better estimates than the iterated Kalman-Bucy smoother.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 44, Issue 7, July 2008, Pages 1676-1685
نویسندگان
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