A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems. Biagetti, G., Crippa, P., Falaschetti, L., & Turchetti, C. In *2019 27th European Signal Processing Conference (EUSIPCO)*, pages 1-5, Sep., 2019.

Paper doi abstract bibtex

Paper doi abstract bibtex

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.

@InProceedings{8902539, author = {G. Biagetti and P. Crippa and L. Falaschetti and C. Turchetti}, booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)}, title = {A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems}, year = {2019}, pages = {1-5}, 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.}, keywords = {learning systems;nonlinear systems;polynomials;principal component analysis;dynamical nonlinear systems;machine learning;feature space;principal component analysis;nonlinear time dependent identification problem;nonlinear input-output function;particle-Bernstein polynomials;input-output relationship;nonlinear audio amplifiers;input-output finite datasets;Nonlinear systems;Training;Machine learning;Principal component analysis;Data models;Predictive models;Time-domain analysis;Machine learning;nonlinear systems;PCA;identification}, doi = {10.23919/EUSIPCO.2019.8902539}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532738.pdf}, }

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