Nonlinear state space model identification using a regularized basis function expansion

@article{Svensson2015NonlinearSS,
  title={Nonlinear state space model identification using a regularized basis function expansion},
  author={Andreas Svensson and Thomas Bo Sch{\"o}n and A. Solin and Simo S{\"a}rkk{\"a}},
  journal={2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
  year={2015},
  pages={481-484}
}
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… 

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