TY - GEN
T1 - Data-driven approximation of differential inclusions and application to detection of transportation modes
AU - Aubin-Frankowski, Pierre Cyril
AU - Petit, Nicolas
N1 - Publisher Copyright:
© 2020 EUCA.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - This article applies the Support Vector Data Description (SVDD) algorithm to approximate the graph of differential inclusions. It is proven that Gaussian SVDD can recover any compact graph if a large enough dataset is available. This data-driven approach can be used to identify discrete-valued parameters of nonlinear dynamical systems with unknown input signal. For illustration, the presented method is applied here both on real and synthetic data for detection of transportation modes based on linear velocity measurements.
AB - This article applies the Support Vector Data Description (SVDD) algorithm to approximate the graph of differential inclusions. It is proven that Gaussian SVDD can recover any compact graph if a large enough dataset is available. This data-driven approach can be used to identify discrete-valued parameters of nonlinear dynamical systems with unknown input signal. For illustration, the presented method is applied here both on real and synthetic data for detection of transportation modes based on linear velocity measurements.
UR - https://www.scopus.com/pages/publications/85090161586
U2 - 10.23919/ecc51009.2020.9143694
DO - 10.23919/ecc51009.2020.9143694
M3 - Conference contribution
AN - SCOPUS:85090161586
T3 - European Control Conference 2020, ECC 2020
SP - 1358
EP - 1364
BT - European Control Conference 2020, ECC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th European Control Conference, ECC 2020
Y2 - 12 May 2020 through 15 May 2020
ER -