Nonlinear predictive model selection and model averaging using information criteria
Authors:
Open Access
- 1 January 2018
- journal article
- research article
- Published by Taylor & Francis in Systems Science & Control Engineering
- Vol. 6 (1) , 319-328
Abstract
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 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.Keywords
Funding Information
- EPSRC Platform Grant (EP/H00453X/1)
- by EU Horizon 2020 Research and Innovation Programme Action Framework (637302)
- Engineering and Physical Sciences Research Council (EP/I011056/1)
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