Zobrazeno 1 - 10
of 3 761
pro vyhledávání: '"Nonlinear State Space Models"'
Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, location, wear, or aging. To provide this information, online inference and
Externí odkaz:
http://arxiv.org/abs/2409.09331
In this paper, we analyze a large class of general nonlinear state-space models on a state-space X, defined by the recursion $\phi_{k+1} = F(\phi_k,\alpha(\phi_k,U_{k+1}))$, $k \in\bN$, where $F,\alpha$ are some functions and $\{U_{k+1}\}_{k\in\bN}$
Externí odkaz:
http://arxiv.org/abs/2402.06447
In this paper, we use the optimization formulation of nonlinear Kalman filtering and smoothing problems to develop second-order variants of iterated Kalman smoother (IKS) methods. We show that Newton's method corresponds to a recursion over affine sm
Externí odkaz:
http://arxiv.org/abs/2306.09148
Publikováno v:
In Mechanical Systems and Signal Processing 1 April 2024 211
Publikováno v:
In Measurement January 2024 224
In this article, we introduce parallel-in-time methods for state and parameter estimation in general nonlinear non-Gaussian state-space models using the statistical linear regression and the iterated statistical posterior linearization paradigms. We
Externí odkaz:
http://arxiv.org/abs/2207.00426
We consider the problem of state estimation in general state-space models using variational inference. For a generic variational family defined using the same backward decomposition as the actual joint smoothing distribution, we establish for the fir
Externí odkaz:
http://arxiv.org/abs/2206.00319
The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse input-output measurements. A fundamental challenge of this problem is that very few to zero prior knowledge is available on both the state and
Externí odkaz:
http://arxiv.org/abs/2206.04791
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connec
Externí odkaz:
http://arxiv.org/abs/2012.05072
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable as it inv
Externí odkaz:
http://arxiv.org/abs/2002.02620