کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7108162 1460619 2018 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum likelihood identification of stable linear dynamical systems
ترجمه فارسی عنوان
شناسایی حداکثر احتمال سیستم های پایدار خطی دینامیکی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی
This paper concerns maximum likelihood identification of linear time invariant state space models, subject to model stability constraints. We combine Expectation Maximization (EM) and Lagrangian relaxation to build tight bounds on the likelihood that can be optimized over a convex parametrization of all stable linear models using semidefinite programming. In particular, we propose two new algorithms: EM with latent States & Lagrangian relaxation (EMSL), and EM with latent Disturbances & Lagrangian relaxation (EMDL). We show that EMSL provides tighter bounds on the likelihood when the effect of disturbances is more significant than the effect of measurement noise, and EMDL provides tighter bounds when the situation is reversed. We also show that EMDL gives the most broadly applicable formulation of EM for identification of models with singular disturbance covariance. The two new algorithms are validated with extensive numerical simulations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 96, October 2018, Pages 280-292
نویسندگان
, , , ,