IMPROVED MODEL ORDER ESTIMATION FOR NONLINEAR DYNAMIC SYSTEMS - Sragner, Laszlo; Horvath, Gabor (International Journal of Computing, 2014)
In system modeling the choice of proper model structure is an essential task. Model structure is defined if both the model class and the size of the model within this class are determined. In dynamic system modeling model size is mainly determined by model order. The paper deals with the question of model order estimation when neural networks are used for modeling nonlinear dynamic systems. One of the possible ways of estimating the order of a neural model is the application of Lipschitz quotient. Although it is easy to use this method, its main drawback is the high sensitivity to noisy data. The paper proposes a new way to reduce the effect of noise. The idea of the proposed method is to combine the original Lipschitz method and the Errors In Variables (EIV) approach. The paper presents the details of the proposed combined method and gives the results of an extensive experimental study.