Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring
This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …
Finite sample analysis of stochastic system identification
In this paper, we analyze the finite sample complexity of stochastic system identification
using modern tools from machine learning and statistics. An unknown discrete-time linear …
using modern tools from machine learning and statistics. An unknown discrete-time linear …
Learning linear dynamical systems with semi-parametric least squares
M Simchowitz, R Boczar… - Conference on Learning …, 2019 - proceedings.mlr.press
We analyze a simple prefiltered variation of the least squares estimator for the problem of
estimation with biased,\emph {semi-parametric} noise, an error model studied more broadly …
estimation with biased,\emph {semi-parametric} noise, an error model studied more broadly …
Subspace-based fault detection algorithms for vibration monitoring
M Basseville, M Abdelghani, A Benveniste - Automatica, 2000 - Elsevier
We address the problem of detecting faults modeled as changes in the eigenstructure of a
linear dynamical system. This problem is of primary interest for structural vibration …
linear dynamical system. This problem is of primary interest for structural vibration …
Dynamic modeling and advanced control of air conditioning and refrigeration systems
BP Rasmussen, AG Alleyne - Air Conditioning and Refrigeration …, 2006 - ideals.illinois.edu
Over 15 billion dollars is spent on energy for residential air-conditioning alone each year,
and air conditioning remains the largest source of peak electrical demand. Improving the …
and air conditioning remains the largest source of peak electrical demand. Improving the …
SPC: Subspace predictive control
Subspace identification has proven to be an excellent system identification method under
peculiar industrial situations. Model predictive control on the other hand also turned out to …
peculiar industrial situations. Model predictive control on the other hand also turned out to …
Algorithms for subspace state-space system identification: an overview
B De Moor, P Van Overschee, W Favoreel - Applied and Computational …, 1999 - Springer
We give a general overview of the state of the art in subspace system identification methods.
We have restricted ourselves to the most important ideas and developments since the …
We have restricted ourselves to the most important ideas and developments since the …
Nuclear norm system identification with missing inputs and outputs
We present a system identification method for problems with partially missing inputs and
outputs. The method is based on a subspace formulation and uses the nuclear norm …
outputs. The method is based on a subspace formulation and uses the nuclear norm …
Statistical model-based damage detection and localization: subspace-based residuals and damage-to-noise sensitivity ratios
M Basseville, L Mevel, M Goursat - Journal of sound and vibration, 2004 - Elsevier
The vibration-based structural health monitoring problem is addressed as the double task of
detecting damages modelled as changes in the eigenstructure of a linear dynamic system …
detecting damages modelled as changes in the eigenstructure of a linear dynamic system …
[图书][B] Control and estimation strategies applied to the activated sludge process
CF Lindberg - 1997 - it.uu.se
Two strategies for designing a DO controller are developed and practically evaluated. The
basic idea is to explicitly take the nonlinear oxygen transfer function into account in the …
basic idea is to explicitly take the nonlinear oxygen transfer function into account in the …