Inference with deep Gaussian process state space models
In this paper, we address the problem of sequential processing of observations modeled by
deep Gaussian process state space models. First, we introduce the model where the Gaus …
deep Gaussian process state space models. First, we introduce the model where the Gaus …
An expert data-driven air combat maneuver model learning approach
View Video Presentation: https://doi. org/10.2514/6.2021-0526. vid This paper considers the
problem of a learning air combat maneuver model when an expert pilot's trajectories are …
problem of a learning air combat maneuver model when an expert pilot's trajectories are …
An efficient two‐stage algorithm for parameter identification of non‐linear state‐space models‐based on Gaussian process regression
X Li, P Ma, Z Wu, T Chao… - IET Control Theory & …, 2023 - Wiley Online Library
This paper aims to improve the efficiency of parameter identification of the nonlinear state‐
space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) …
space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) …
Recursive Gaussian Process State Space Model
T Zheng, L Cheng, S Gong, X Huang - arXiv preprint arXiv:2411.14679, 2024 - arxiv.org
Learning dynamical models from data is not only fundamental but also holds great promise
for advancing principle discovery, time-series prediction, and controller design. Among …
for advancing principle discovery, time-series prediction, and controller design. Among …
Bayesian Nonparametric State-Space Model for System Identification with Distinguishable Multimodal Dynamics
The goal of system identification is to learn about underlying physics dynamics behind the
time-series data. To model the probabilistic and nonparametric dynamics, Gaussian process …
time-series data. To model the probabilistic and nonparametric dynamics, Gaussian process …
Review of the application of modeling and estimation method in system identification for nonlinear state-space models
X Li, P Ma, T Chao, M Yang - International Journal of Modeling …, 2024 - World Scientific
Nonlinear state-space models (SSMs) are widely used to model actual industrial processes.
System identification is an important method to reduce the uncertainty of the simulation …
System identification is an important method to reduce the uncertainty of the simulation …