Abstract
ABSTRACTThis 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 model averaging is evaluated via a number of case studies using both simulation and real data. The results show that APRESS produces better models in terms of generalization performance and model complexity.
Highlights
Model selection plays a fundamental role in choosing a best model from a series of candidate models for datadriven modelling and system identification problems
This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems
Akaike information criterion, Bayesian information criterion and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and model averaging is evaluated via a number of case studies using both simulation and real data
Summary
Model selection plays a fundamental role in choosing a best model from a series of candidate models for datadriven modelling and system identification problems. AIC and BIC can usually produce good model selection result based on the assumption that the ‘true’ model is among the candidate models, they may fail to select the best model when the system is very complex and neither of the candidate models can sufficiently represent the data. These situations often occur when the model structure or some prior information is unknown. It is essential to investigate AIC, BIC and APRESS, to figure out which one works better for model selection of nonlinear system identification and data-driven modelling problems.
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