Estimation of dual‐mode nonlinear stochastic systems with unknown parameters

DOI10.1002/rnc.6331zbMath1529.93101MaRDI QIDQ6139888

Unnamed Author, Linfeng Xu, Unnamed Author, Yan Liang

Publication date: 19 December 2023

Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)

zbMATH Keywords

state estimationBayesian learningsequential Monte Carlodual-mode nonlinear systems

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10)

Cites Work

• Unnamed Item
• Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
• Mode separability-based state estimation for uncertain constrained dynamic systems
• A flexible state-space model for learning nonlinear dynamical systems
• Online Bayesian inference and learning of Gaussian-process state-space models
• Security distributed state estimation for nonlinear networked systems against DoS attacks
• The interacting multiple model algorithm for systems with Markovian switching coefficients
• A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
• Approximate Inference in State-Space Models With Heavy-Tailed Noise
• Modeling and State Estimation for Dynamic Systems With Linear Equality Constraints
• Bayesian Learning of Degenerate Linear Gaussian State Space Models Using Markov Chain Monte Carlo
• Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
• Joint attack detection and secure state estimation of cyber‐physical systems
• A particle filter via constrained sampling for nonlinear dynamic systems
• Diagnosis and Fault-Tolerant Control

This page was built for publication: Estimation of dual‐mode nonlinear stochastic systems with unknown parameters