Nonlinear filtering by kriging, with application to system inversion

Prediction by kriging does not rely on any specific model structure, and is thus much more flexible than approaches based on parametric behavioural models. Since accurate predictions are obtained for extremely short training sequences, it generally performs better than prediction methods using parametric models. Application to nonlinear system inversion is considered