Use of the Analysis of Variance Method for Model Order Selection of N-FIR Models

Identification of non-linear FIR-models is studied. In particular the selection of model structure, i.e., to find the contributing input time lags, has been examined. A common method, exhaustive search among models with all possible combinations of the input time lags, has some undesired drawbacks, as a tendency that the minimization algorithm gets stuck in local minima and heavy computations. To avoid these drawbacks we need to know the model structure prior to identifying a model. In this report we show that a statistical method, the multivariate analysis of variance, is a good alternative to exhaustive search in the identification of the structure of non-linear FIR-models. We can reduce the risks of getting an erroneous model structure due to the non-convexity of the minimization problems, reduce the computation time needed and also get a good estimate of how far we can enhance the fit of the desired model