This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and determine which one best presents the data. Three commonly used criteria, namely, Akaike information criterion, Bayesian information criterion and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and m…