Add to ORKGSystems in real applications are usually affected by nonlinear couplings, external disturbances, and unmodeled dynamics that may have significant impact on the achievable control performance. Gaussian process (GP) regression is an effective tool to build the nonparametric, probabilistic model directly from input/output data. In this work, we introduce a new strategy for efficient Bayesian learning with GP state-space models (GPSSM), where the wide class of nonlinear systems are transformed into linear parameter-varying (LPV) representations at the level of probability density functions (PDF). The proposed strategy can provide computationally efficient state propagation, which significantly benefits the online implementation of model predict...
Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in stat...
State-space models are successfully used in many areas of science, engineering and economics to mode...
State-space models are successfully used in many areas of science, engineer-ing and economics to mod...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
State-space models have been successfully used for more than fifty years in different areas of scien...
State-space models have been successfully used for more than fifty years in differ-ent areas of scie...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Applications to learn control of unfamiliar dynamical systems with increasing autonomy are ubiquitou...
Model predictive control has enjoyed a lot of success in the past half a century due to its ability ...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
Building physics-based models of complex physical systems like buildings and chemical plants is extr...
Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in stat...
State-space models are successfully used in many areas of science, engineering and economics to mode...
State-space models are successfully used in many areas of science, engineer-ing and economics to mod...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
State-space models have been successfully used for more than fifty years in different areas of scien...
State-space models have been successfully used for more than fifty years in differ-ent areas of scie...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
Applications to learn control of unfamiliar dynamical systems with increasing autonomy are ubiquitou...
Model predictive control has enjoyed a lot of success in the past half a century due to its ability ...
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference ...
Building physics-based models of complex physical systems like buildings and chemical plants is extr...
Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, ...
Gaussian process models constitute a class of probabilistic statistical models in which a Gaussian p...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...