Improved model order estimation for nonlinear dynamic systems

In system modelling 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 modelling model size is mainly determined by model order. We deal with the question of model order estimation when neural networks are used for modelling 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. We propose 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. We present the details of the proposed combined method and gives the results of an extensive experimental study