This paper investigates the combination of a previously developed simulation error minimization (SEM) method for NARX model selection with the Least Absolute Shrinkage and Selection Operator (LASSO). The latter introduces a regularization term in the performance index that penalizes model size increase. In the context of SEM-based model selection it can be used both to trim the candidate regressor set and to provide model pruning in the model construction phase. It is shown that the LASSO-enhanced SEM method significantly reduces the computational effort and provides at least as accurate model selection as the plain SEM method.
LASSO-enhanced simulation error minimization method for NARX model selection
PIRODDI, LUIGI
2010-01-01
Abstract
This paper investigates the combination of a previously developed simulation error minimization (SEM) method for NARX model selection with the Least Absolute Shrinkage and Selection Operator (LASSO). The latter introduces a regularization term in the performance index that penalizes model size increase. In the context of SEM-based model selection it can be used both to trim the candidate regressor set and to provide model pruning in the model construction phase. It is shown that the LASSO-enhanced SEM method significantly reduces the computational effort and provides at least as accurate model selection as the plain SEM method.File | Dimensione | Formato | |
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