Incremental Identification of Narx Models by Sparse Grid Approximation
نویسندگان
چکیده
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. Copyright c ©2005 IFAC
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