Abstract

This article presents an algorithm for identification of nonlinear state-space models when the “true” model structure of a process is unknown. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. An approximation of the model structure is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through an example.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call