کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4628292 1631821 2014 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sequential Monte Carlo sampling in hidden Markov models of nonlinear dynamical systems
ترجمه فارسی عنوان
روش نمونه گیری تصادفی مونت کارلو در مدل های پنهان مارکوف از سیستم های دینامیکی غیر خطی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
چکیده انگلیسی

We investigate the issue of which state functionals can have their uncertainty estimated efficiently in dynamical systems with uncertainty. Because of the high dimensionality and complexity of the problem, sequential Monte Carlo (SMC) methods are used. We investigate SMC methods where the proposal distribution is computed by maximum likelihood or by a linearization approach. We prove that the variance of the SMC method is bounded linearly in the number of time steps when the proposal distribution is truncated normal distribution. We also show that for a moderate large number of steps the error produced by approximation of dynamical systems linearly accumulates on the condition that the logarithm of the density function of noise is Lipschitz continuous. This finding is significant because the uncertainty in many dynamical systems, in particular, in chemical engineering systems, can be assumed to have this nature. We demonstrate our findings for a simple test case from chemical engineering. The theoretical findings provide a foundation for the parallel software SISTOS.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 233, 1 May 2014, Pages 507–521
نویسندگان
, ,