Online system identification in a Duffing oscillator by free energy minimisation
This addresses parameter estimation in dynamical systems for applications like control or monitoring, but it is incremental as it applies an existing method to a specific oscillator.
The paper tackled online system identification for a Duffing oscillator by casting it as a generative model and using variational message passing, achieving performance comparable to offline prediction error minimization in a state-of-the-art nonlinear model.
Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals. Here, the nonlinear stochastic differential equation of a Duffing oscillator is cast to a generative model and dynamical parameters are inferred using variational message passing on a factor graph of the model. The approach is validated with an experiment on data from an electronic implementation of a Duffing oscillator. The proposed inference procedure performs as well as offline prediction error minimisation in a state-of-the-art nonlinear model.