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Found 21 papers, 0 papers with code
Harnessing the Final Control Error for Optimal Data-Driven Predictive Control
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on accurate models poses many limitations in real-world applications.
Dynamic Brain Networks with Prescribed Functional Connectivity
In this paper, we consider stable stochastic linear systems modeling whole-brain resting-state dynamics.
On the impact of regularization in data-driven predictive control
Model predictive control (MPC) is a control strategy widely used in industrial applications.
Uncertainty-aware data-driven predictive control in a stochastic setting
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant.
Data-driven predictive control in a stochastic setting: a unified framework
Data-driven predictive control (DDPC) has been recently proposed as an effective alternative to traditional model-predictive control (MPC) for its unique features of being time-efficient and unbiased with respect to the oracle solution.
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection
After modeling the signals, we use an anomaly detection system based on the classic CUMSUM algorithm and a variational approximation of the $f$-divergence to detect both isolated point anomalies and change-points in statistics of the signals.
A novel Deep Neural Network architecture for non-linear system identification
We present a novel Deep Neural Network (DNN) architecture for non-linear system identification.
Estimating Koopman operators for nonlinear dynamical systems: a nonparametric approach
The Koopman operator is a mathematical tool that allows for a linear description of non-linear systems, but working in infinite dimensional spaces.
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in robotics.
Online semi-parametric learning for inverse dynamics modeling
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model.
On-line Bayesian System Identification
We consider an on-line system identification setting, in which new data become available at given time steps.
Maximum Entropy Vector Kernels for MIMO system identification
In this paper, adopting Maximum Entropy arguments, we derive a new $\ell_2$ penalty deriving from a vector-valued kernel; to do so we exploit the structure of the Hankel matrix, thus controlling at the same time complexity, measured by the McMillan degree, stability and smoothness of the identified models.
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
In this paper, a comparative study of estimators based on these different types of regularizers is reported.
Identification of stable models via nonparametric prediction error methods
Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system.
Texture Representations for Image and Video Synthesis
In texture synthesis and classification, algorithms require a small texture to be provided as an input, which is assumed to be representative of a larger region to be re-synthesized or categorized.
A Bayesian Approach to Sparse plus Low rank Network Identification
We consider the problem of modeling multivariate time series with parsimonious dynamical models which can be represented as sparse dynamic Bayesian networks with few latent nodes.
Visual Representations: Defining Properties and Deep Approximations
Visual representations are defined in terms of minimal sufficient statistics of visual data, for a class of tasks, that are also invariant to nuisance variability.
Bayesian and regularization approaches to multivariable linear system identification: the role of rank penalties
Recent developments in linear system identification have proposed the use of non-parameteric methods, relying on regularization strategies, to handle the so-called bias/variance trade-off.
A scaled gradient projection method for Bayesian learning in dynamical systems
If the unknown impulse response is modeled as a Gaussian process with a suitable kernel, the maximization of the marginal likelihood leads to a challenging nonconvex optimization problem, which requires a stable and effective solution strategy.
Controlled Recognition Bounds for Visual Learning and Exploration
We describe the tradeoff between the performance in a visual recognition problem and the control authority that the agent can exercise on the sensing process.
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors
We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems.
