In this paper an offline approach for output-only Bayesian identification of stochastic
nonlinear systems is presented. The approach is based on a re-parameterization of the joint …

Abstract System identification of dynamical systems is often posed as a least squares
minimization problem. The aim of these optimization problems is typically to learn either …

It is necessary to investigate the identification of structural systems and unknown inputs
under non‐Gaussian measurement noises. In recent years, a few scholars have proposed …

The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional
meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter …

A few unscented Kalman filters (UKFs) have been developed for simultaneous state-
parameter-input estimation, however, these UKFs often have at least one of these …

Soil constitutive model parameters can be identified from triaxial test data. The identification
is frequently performed by fitting a constitutive model to triaxial test data from a purely …

This paper presents the application of nonlinear Bayesian filtering–based system
identification (SI) methods when employed to estimate the parameters of stochastic …

The identification of parameters of linear or nonlinear systems under unknown inputs and
limited outputs is an important but still challenging topic in the context of structural health …

Particle filter (PF) is usually used for identifying structural parameters in nonlinear systems.
However, the traditional PF method requires that all the external excitations are available or …

Oil dampers have been used in recent years for passive structural control and shock
mitigation in dynamic structural systems. However, machining technical and economic …