Bayesian topology identification of linear dynamic networks
For researchers in system identification, this offers a Bayesian approach to network topology identification, but the results are preliminary and lack quantitative comparison.
The paper proposes a Bayesian model selection method for identifying the interconnection structure of linear dynamic networks without estimating the dynamics, using Gaussian processes and expectation-maximization. Numerical results show effectiveness, but no concrete performance numbers are provided.
In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method.