Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT
COVID-19 has disrupted normal life and has enforced a substantial change in the policies,
priorities and activities of individuals, organisations and governments. These changes are …
priorities and activities of individuals, organisations and governments. These changes are …
Kernel methods in system identification, machine learning and function estimation: A survey
Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …
Regularization and Bayesian learning in dynamical systems: Past, present and future
A Chiuso - Annual Reviews in Control, 2016 - Elsevier
Regularization and Bayesian methods for system identification have been repopularized in
the recent years, and proved to be competitive wrt classical parametric approaches. In this …
the recent years, and proved to be competitive wrt classical parametric approaches. In this …
A shift in paradigm for system identification
System identification is a mature research area with well established paradigms, mostly
based on classical statistical methods. Recently, there has been considerable interest in so …
based on classical statistical methods. Recently, there has been considerable interest in so …
System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques
Model estimation and structure detection with short data records are two issues that receive
increasing interests in System Identification. In this paper, a multiple kernel-based …
increasing interests in System Identification. In this paper, a multiple kernel-based …
Full Bayesian identification of linear dynamic systems using stable kernels
G Pillonetto, L Ljung - … of the National Academy of Sciences, 2023 - National Acad Sciences
System identification learns mathematical models of dynamic systems starting from input–
output data. Despite its long history, such research area is still extremely active. New …
output data. Despite its long history, such research area is still extremely active. New …
On kernel design for regularized LTI system identification
T Chen - Automatica, 2018 - Elsevier
There are two key issues for the kernel-based regularization method: one is how to design a
suitable kernel to embed in the kernel the prior knowledge of the LTI system to be identified …
suitable kernel to embed in the kernel the prior knowledge of the LTI system to be identified …
An industrial case study on the combined identification and offset-free control of a chemical process
SJ Kuntz, JJ Downs, SM Miller, JB Rawlings - Computers & Chemical …, 2023 - Elsevier
For three decades, model predictive control (MPC) has been the flagship advanced control
method in the chemical process industries. However, most implementations still use …
method in the chemical process industries. However, most implementations still use …
Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests
Uncertainty analysis of the identified hydrodynamic coefficients of a nonlinear manoeuvring
model is presented in this paper. The classical parameter estimation method, Least Square …
model is presented in this paper. The classical parameter estimation method, Least Square …
An efficient implementation for spatial–temporal Gaussian process regression and its applications
Spatial–temporal Gaussian process regression is a popular method for spatial–temporal
data modeling. Its state-of-art implementation is based on the state-space model realization …
data modeling. Its state-of-art implementation is based on the state-space model realization …