Olaf Kahrs, Marc Brendel, Wolfgang Marquardt:
Incremental identification of NARX models by sparse grid approximation
16th IFAC World Congress, Prague, Czech Republic
Nonlinear empirical models are used in various applications. During model-building, five major steps usually have to be carried out: model structure selection, determination of input variables, complexity adjustment of the model, parameter estimation and model validation. These steps have to be repeated until a satisfactory model is found, which can be very time consuming and may require user interaction. This paper proposes an algorithm based on sparse grid function approximation to incrementally build a nonlinear empirical model. The algorithm exhibits good performance in terms of manual effort and computation time. The method is illustrated by a case study on the identification of a NARX model.
function approximation, nonlinear models, sparse grids, input selection, NARX