Controllability Gramian of Nonlinear Gaussian Process State Space Models with Application to Model Sparsification
نویسندگان
چکیده
منابع مشابه
Identification of Gaussian Process State Space Models
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in GPSSMs has not been satisfactorily addressed yet: system identification, i.e...
متن کاملVariational Gaussian Process State-Space Models
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer th...
متن کاملComputationally Efficient Bayesian Learning of Gaussian Process State Space Models
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models ca...
متن کاملState Space Gaussian Process Prediction
Learning accurate models of complex clinical time-series data is critical for understanding the disease and its dynamics. Modeling of clinical time-series is particularly challenging because: observations are made at irregular time intervals and may be missing for long periods of time. In this work, we propose a new model of clinical time series data that is optimized to handle irregularly samp...
متن کاملOn-line ltering for nonlinear/ non-Gaussian state space models
The bootstrap lter is an algorithm for implementing recursive Bayesian lters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise. In situations where there is low overlap between prior and posterior, the standard bootstrap lter may not wo...
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.2020.12.215