کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7180975 | 1467867 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In the last 20 years the applicability of Bayesian inference to the system identification of structurally dynamical systems has been helped considerably by the emergence of Markov chain Monte Carlo (MCMC) algorithms - stochastic simulation methods which alleviate the need to evaluate the intractable integrals which often arise during Bayesian analysis. In this paper specific attention is given to the situation where, with the aim of performing Bayesian system identification, one is presented with very large sets of training data. Building on previous work by the author, an MCMC algorithm is presented which, through combing Data Annealing with the concept of 'highly informative training data', can be used to analyse large sets of data in a computationally cheap manner. The new algorithm is called Smooth Data Annealing.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Probabilistic Engineering Mechanics - Volume 42, October 2015, Pages 54-63
Journal: Probabilistic Engineering Mechanics - Volume 42, October 2015, Pages 54-63
نویسندگان
P.L. Green,