A latent restoring force approach to nonlinear system identification

Identification of nonlinear dynamic systems remains a significant challenge across engineering. This work suggests an approach based on Bayesian filtering to extract and identify the contribution unknown term in system which can be seen as alternative viewpoint restoring force surface type approaches. To achieve this identification, is modelled, initially, Gaussian process time. That converted into state-space model combined with linear component system. Then, by inference smoothing distributions, internal states extracted. In possession these constructed. The demonstrated effective both simulated case study experimental benchmark dataset.