Regularized System Identification: A Hierarchical Bayesian Approach
نویسندگان
چکیده
منابع مشابه
A Hierarchical Bayesian Approach
9 Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by 10 NASA’s Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves Aerosol 11 Optical Depth (AOD) at 17.6 km resolution. A systematic study of aerosols and their impact on public health, espe12 cially in highly-populated urban areas, requires...
متن کاملA Bayesian Approach to System Identification using Markov Chain Methods
This paper takes a Bayesian approach to the problem of dynamic system estimation, and illustrates how posterior densities for rather arbitrary system parameters or properties (such a frequency response, phase margin etc) may be numerically computed. In achieving this, the key idea of constructing an ergodic Markov chain with invariant distribution equal to the desired posterior is one borrowed ...
متن کاملOutlier robust system identification: a Bayesian kernel-based approach
In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. To build robustness to outliers, we model the measurement noise as...
متن کاملA Bayesian Approach to Closed-loop System Identification
The challenging issue of identifying a closed-loop system from short and/or non-informative data records is addressed. A bayesian approach is developed within this framework. It is shown that accurate estimates and realistic confidence intervals can be obtained by taking into account prior knowledge on the system. The performances of the proposed method are illustrated with a simulation example...
متن کاملA semiparametric Bayesian approach to Wiener system identification
We consider a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a state-space model for the linear dynamical system and a nonparametric Gaussian process (GP) model for the static nonlinearity. The GP model is a exible model that can describe di erent types of nonlinearities while avoiding making strong assumptions such as monotonicity. We derive an inferent...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2020
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2020.12.200