The aim of this paper is to present a general machine learning approach to the identification of nonlinear systems, using the observed input-output finite datasets. The approach is derived representing the input and output signals in the feature space by the principal component analysis (PCA), thus transforming the nonlinear time dependent identification problem to the regression of a nonlinear input-output function. To face this problem an effective machine learning technique based on particle-Bernstein polynomials has been used to model the input-output relationship that describes the system. The approach has been validated by identifying two real world nonlinear systems, in the fields of speech signals and nonlinear audio amplifiers.

A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems / Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio. - ELETTRONICO. - (2019), pp. 1-5. (Intervento presentato al convegno 2019 27th European Signal Processing Conference (EUSIPCO) tenutosi a A Coruna, Spain nel 2 - 6 September 2019) [10.23919/EUSIPCO.2019.8902539].

A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems

Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti
2019-01-01

Abstract

The aim of this paper is to present a general machine learning approach to the identification of nonlinear systems, using the observed input-output finite datasets. The approach is derived representing the input and output signals in the feature space by the principal component analysis (PCA), thus transforming the nonlinear time dependent identification problem to the regression of a nonlinear input-output function. To face this problem an effective machine learning technique based on particle-Bernstein polynomials has been used to model the input-output relationship that describes the system. The approach has been validated by identifying two real world nonlinear systems, in the fields of speech signals and nonlinear audio amplifiers.
2019
978-9-0827-9703-9
978-90-827970-2-2
978-1-5386-7300-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/272051
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