System-Identification

We propose a computational procedure for identifying convection in heat transfer dynamics based on a Gaussian process latent force model.

We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model.

In the Bayesian approach to modelling, noise is not considered a nuisance but rather a reflection of uncertainty, expressed in terms of probability distributions. Variational Bayesian inference optimizes an approximation of the posterior distribution, allowing for a trade-off between accuracy and computational workload. In this talk, I will demonstrate how this technique may be applied in a simple system identification problem, compare it to classical techniques, and illustrate how modularity may be utilized to accelerate the model design cycle.

We propose a variational message passing inference algorithm for online system identification using polynomial NARMAX models. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.

A paper on online system identification, where I cast the nonlinear stochastic differential equation of a Duffing oscillator to a probabilistic graphical model and use variational message passing to infer dynamical parameters of the system.