  This paper is concerned with black-box identification of nonlinear state
space models. By using a basis function expansion within the state space model,
we obtain a flexible structure. The model is identified using an expectation
maximization approach, where the states and the parameters are updated
iteratively in such a way that a maximum likelihood estimate is obtained. We
use recent particle methods with sound theoretical properties to infer the
states, whereas the model parameters can be updated using closed-form
expressions by exploiting the fact that our model is linear in the parameters.
Not to over-fit the flexible model to the data, we also propose a
regularization scheme without increasing the computational burden. Importantly,
this opens up for systematic use of regularization in nonlinear state space
models. We conclude by evaluating our proposed approach on one simulation
example and two real-data problems.
