We propose two optimization-based heuristics for structure selection and identification of PieceWise Affine (PWA) models with exogenous inputs. The first method determines the number of affine sub-models assuming known model order of the sub-models, while the second approach estimates the model order for a given number of affine sub-models. Both approaches rely on the use of regularization-based shrinking strategies, that are exploited within a coordinate-descent identification algorithm. This allows us to estimate the structure of the PWA models along with its model parameters. Starting from an overparameterized model, the key idea is to alternate between an identification step and structure refinement. The performance of the presented strategies is assessed over two benchmark examples.

Shrinkage Strategies for Structure Selection and Identification of Piecewise Affine Models

Breschi V.;
2020-01-01

Abstract

We propose two optimization-based heuristics for structure selection and identification of PieceWise Affine (PWA) models with exogenous inputs. The first method determines the number of affine sub-models assuming known model order of the sub-models, while the second approach estimates the model order for a given number of affine sub-models. Both approaches rely on the use of regularization-based shrinking strategies, that are exploited within a coordinate-descent identification algorithm. This allows us to estimate the structure of the PWA models along with its model parameters. Starting from an overparameterized model, the key idea is to alternate between an identification step and structure refinement. The performance of the presented strategies is assessed over two benchmark examples.
2020
Proceedings of the IEEE Conference on Decision and Control
978-1-7281-7447-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167003
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