Add to ORKGWe investigate the accuracy of inference in a chaotic dynamical sys- tem (Duffing oscillator) with the Unscented Kalman Filter, and quantify the dependence on the sample size, the signal to noise ratio and the initialization
The conventional unscented Kalman filter (UKF) requires prior knowledge on system noise statistics. ...
When compared to independent harmonic or stochastic excitation, there exist relatively few methods ...
Nonlinear estimators based on the Kalman filter, the extended Kalman filter (EKF) and unscented Kalm...
We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the...
The aim of the research concerns inference methods for non-linear dynamical systems. In particular, ...
We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the...
Abstract This paper examines and contrasts the feasi-bility of joint state and parameter estimation ...
Author name used in this publication: Chi K. TseRefereed conference paper2006-2007 > Academic resear...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
The Duffing oscillator remains a key benchmark in nonlinear systems analysis and poses interesting c...
System identification is often limited to parameter identification, while model uncertainties are d...
The filtering distribution is a time-evolving probability distribution on the state of a dynamical s...
Sequential inference methods have played a crucial role in many of the technological marvels that we...
The prediction of a single observable time series has been achieved with varying degrees of success....
The filtering distribution is a time-evolving probability distribution on the state of a dynamical s...
The conventional unscented Kalman filter (UKF) requires prior knowledge on system noise statistics. ...
When compared to independent harmonic or stochastic excitation, there exist relatively few methods ...
Nonlinear estimators based on the Kalman filter, the extended Kalman filter (EKF) and unscented Kalm...
We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the...
The aim of the research concerns inference methods for non-linear dynamical systems. In particular, ...
We investigate the accuracy of inference in a chaotic dynamical system (Duffing oscillator) with the...
Abstract This paper examines and contrasts the feasi-bility of joint state and parameter estimation ...
Author name used in this publication: Chi K. TseRefereed conference paper2006-2007 > Academic resear...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
The Duffing oscillator remains a key benchmark in nonlinear systems analysis and poses interesting c...
System identification is often limited to parameter identification, while model uncertainties are d...
The filtering distribution is a time-evolving probability distribution on the state of a dynamical s...
Sequential inference methods have played a crucial role in many of the technological marvels that we...
The prediction of a single observable time series has been achieved with varying degrees of success....
The filtering distribution is a time-evolving probability distribution on the state of a dynamical s...
The conventional unscented Kalman filter (UKF) requires prior knowledge on system noise statistics. ...
When compared to independent harmonic or stochastic excitation, there exist relatively few methods ...
Nonlinear estimators based on the Kalman filter, the extended Kalman filter (EKF) and unscented Kalm...