Results 211 to 220 of about 1,180,844 (267)
A music source separation method integrating time-frequency decoupling and mamba-based state space modeling. [PDF]
Sci RepZhang C, Zheng J, Cao M.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Completing the State Space with Subjective States
Journal of Economic Theory, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
SIAM Review, 1973
Summary: A resolution space, recently introduced for the study of causality in an operator theoretic setting, is employed to formulate an abstract state concept which generalizes the state space theory commonly used in the study of finite-dimensional dynamical systems.
openaire +2 more sources
Summary: A resolution space, recently introduced for the study of causality in an operator theoretic setting, is employed to formulate an abstract state concept which generalizes the state space theory commonly used in the study of finite-dimensional dynamical systems.
openaire +2 more sources
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
The Kalman filter is the linear optimal estimator for random signals. We develop state-space RLS that is the counterpart of the Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares.
openaire +1 more source
The Kalman filter is the linear optimal estimator for random signals. We develop state-space RLS that is the counterpart of the Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares.
openaire +1 more source
2010
A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
openaire +1 more source
A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
openaire +1 more source
THE LOGIC OF INFORMATION IN STATE SPACES
The Review of Symbolic Logic, 2020AbstractState spaces are, in the most general sense, sets of entities that contain information. Examples include states of dynamical systems, processes of observations, or possible worlds. We use domain theory to describe the structure of positive and negative information in state spaces.
openaire +3 more sources
The statistics of state-spaces
Annals of Mathematics and Artificial Intelligence, 1990The state-space model is a general, powerful, and elegant representation of problem solving. Nevertheless, state-spaces have rarely been used to model realistic environments because conventional state-spaces are inherently deterministic, while the world is not.
openaire +2 more sources
2008
State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
openaire +1 more source
State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
openaire +1 more source