System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf]

By Alex Simpkins. IEEE robotics & automation magazine, 2012

1,938 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d53294718a19bb36e66a1688109d856dee870a69

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In Control and Systems Engineering

Citations in the corpus

A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning (2012, background): “It should be noted that a large body of work in control engineering is also devoted to the problem of modeling DS from experimental data (Gevers, 2006; Zadeh, 1956; Aström & Bohlin, 1965; Ljung, 1999; Ho & Kalman, 1966; Söderström & Stoica, 1989).”
Locally Weighted Ridge Regression for Power System Online Sensitivity Identification Considering Data Collinearity (2018, background): “to argument variable variations are sufficient [7], [15].”
Agnostic System Identification for Monte Carlo Planning (2015, background): “Ross and Bagnell (2012) point out that the common “textbook” framework for system identification (e.g. Ljung, 1999), in which a single batch of data is used to train a model which is then used to obtain a policy, can fail catastrophically when the plan enters states that the training set did not…”
Learning predictive models of a depth camera & manipulator from raw execution traces (2014, background): “In the controls community, the problem of inferring the unknown parameters of an input-output system is called system identification [18]–[20].”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, neutral): “Simulations show that the deep learning based flow inference scheme has high true positive rate and low false positive rate, compared to ARMAX-least squares [100], Naive Bayes classifiers and Gaussian mixture models.”
Evaluating the strength of identification in DSGE models. An a priori approach (2010, methodology): “6), can be computed using the following result (see Ljung (1999))”
Kernel methods in system identification, machine learning and function estimation: A survey (2014, methodology): “See, e.g. Ljung (1999) or Ljung (2002) for more details.”
Data center cooling using model-predictive control (2018, background): “, learn the parameters of a dynamics model) in a dedicated exploration phase, while closed-loop strategies control from the outset, updating models along the way [20].”
Estimating electromechanical modes and mode shapes using the multichannel ARMAX model (2013, background): “both off-line and on-line algorithms (see [16] and [20] for details).”
Nonlinear System Identification: A User-Oriented Road Map (2019, methodology): “This comes with the cost of an exploding number of model parameters for a growing complexity, leading to an increased risk of overfitting [6], [7], [30].”
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis (2013, methodology): “The parameter estimation methods have been extensively studied in system identification [64], and its application to FDD was first described by [51] as follows.”
Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes (2013, methodology): “We mitigate the errors-in-variables problem by incorporat ing into system identification the signal pre-processing step that is usually carried out manually [4], [7].”