Sparse Bayesian identification of polynomial NARX models

In this paper a novel sparse Bayesian structure detection algorithm is introduced for the identification of nonlinear autoregressive with exogenous inputs (NARX) dynamic systems. The main advantage of this algorithm over alternatives is that parameter uncertainty is naturally incorporated, and parameter estimation by variational inference is computationally efficient, consisting of a sequence of closed form updates. The proposed framework is demonstrated through a commonly used simulated benchmark problem.