TY - GEN
T1 - Adaptive-Graph Semi-Supervised Dynamical System Identification
AU - Xiong, Yu
AU - Guo, Yu
AU - Hu, Yuxin
AU - Wang, Fei
N1 - Publisher Copyright:
© 2023 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2023
Y1 - 2023
N2 - In this paper, we present a novel adaptive-graph semi-supervised dynamical system identification (AG-SSI) for the challenging nonlinear dynamical system identification. AG-SSI constructs the regressors in the Reproducing Kernel Hilbert Spaces (RKHS) leveraging on both input-output data and input-only data. Then, a manifold regularization with adaptive graph structure is introduced for semi-supervised learning. AG-SSI can jointly learn the optimal affinity graph and identify the unknown dynamical system. An efficient iteration method is proposed to solve this challenging problem, which promotes graph matrix learning and system identification in a mutual reinforcement mode. Experiments are conducted on three nonlinear dynamical systems and the results demonstrate the superiority of the proposed method compared with the existing semi-supervised system identification algorithms.
AB - In this paper, we present a novel adaptive-graph semi-supervised dynamical system identification (AG-SSI) for the challenging nonlinear dynamical system identification. AG-SSI constructs the regressors in the Reproducing Kernel Hilbert Spaces (RKHS) leveraging on both input-output data and input-only data. Then, a manifold regularization with adaptive graph structure is introduced for semi-supervised learning. AG-SSI can jointly learn the optimal affinity graph and identify the unknown dynamical system. An efficient iteration method is proposed to solve this challenging problem, which promotes graph matrix learning and system identification in a mutual reinforcement mode. Experiments are conducted on three nonlinear dynamical systems and the results demonstrate the superiority of the proposed method compared with the existing semi-supervised system identification algorithms.
KW - adaptive graph structure
KW - nonlinear dynamical system identification
KW - semi-supervised learning
UR - https://www.scopus.com/pages/publications/85175552630
U2 - 10.23919/CCC58697.2023.10240035
DO - 10.23919/CCC58697.2023.10240035
M3 - 会议稿件
AN - SCOPUS:85175552630
T3 - Chinese Control Conference, CCC
SP - 1345
EP - 1350
BT - 2023 42nd Chinese Control Conference, CCC 2023
PB - IEEE Computer Society
T2 - 42nd Chinese Control Conference, CCC 2023
Y2 - 24 July 2023 through 26 July 2023
ER -