Automated order determination strategies for nonlinear dynamic models

A crucial part during the generation of nonlinear dynamic models is the determination of an appropriate model order. Five automated order determination strategies are compared. One model-based and four model-free approaches are investigated. We evaluated the performances of all methods with four artificial test processes and two noise levels. In an external dynamics approach, local model networks are trained with the determined (lagged) inputs and outputs that are found through the automated order determination strategies. An independent noise-free data set reveals the simulation quality of the estimated models. Most of the filter methods are unreliable since their performance varies strongly. Most robust is the wrapper method, which achieves good results in general. We show that in some cases even the model yielded through the incorporation of prior-knowledge is outperformed by some of the models resulting from the presented order determination methods.