The aim of this paper is to propose a novel identification aalgorithm based on separable least squares ideas, for a class of nonlinear, possibly parameter-varying, input/output models. These models are given in the form of a Linear Fractional Transformation (LFT) where the "forward" part is represented by a conventional linear regression and the "feedback" part is given by a nonlinear map which can take into account scheduling variables available for measurement. The nonlinear part of the model can be parameterised according to various paradigms, like, e.g., Neural Network (NN) or NARX.

(2003). Identification of nonlinear parametrically varying models using separable least squares . Retrieved from http://hdl.handle.net/10446/86572

Identification of nonlinear parametrically varying models using separable least squares

PREVIDI, Fabio;
2003-01-01

Abstract

The aim of this paper is to propose a novel identification aalgorithm based on separable least squares ideas, for a class of nonlinear, possibly parameter-varying, input/output models. These models are given in the form of a Linear Fractional Transformation (LFT) where the "forward" part is represented by a conventional linear regression and the "feedback" part is given by a nonlinear map which can take into account scheduling variables available for measurement. The nonlinear part of the model can be parameterised according to various paradigms, like, e.g., Neural Network (NN) or NARX.
2003
Previdi, Fabio; Lovera, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/86572
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