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- W2567535352 abstract "This paper explores a combined global and local identification approach for linear parameter-varying systems. Ideally, the combined approach retains advantages of its two extremes - global and local - with the possibility to emphasize one or the other. Practically, it is prone to overfitting. This paper proposes a remedy based on the l 2,1 -norm regularization, describes its implementation within the nonlinear least squares framework, and gives an experimental validation. The results show a substantial decrease in the Euclidean norm of the model parameters, which resulted in a significantly smoother frequency response function surface and in overall, less-deviating model behavior." @default.
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- W2567535352 date "2016-10-01" @default.
- W2567535352 modified "2023-10-01" @default.
- W2567535352 title "Experimental validation of a combined global and local LPV system identification approach with ℓ<inf>2,1</inf>-norm regularization" @default.
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- W2567535352 doi "https://doi.org/10.1109/iecon.2016.7793588" @default.
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