Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior
Summary :
This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.10 pp.2095-2105
- Publication Date
- 2017/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.2095
- Type of Manuscript
- PAPER
- Category
- Systems and Control
Authors
Thomas WILHELEM
the University of Nagoya
Hiroyuki OKUDA
the University of Nagoya
Tatsuya SUZUKI
the University of Nagoya
Keyword
Latest Issue
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Thomas WILHELEM, Hiroyuki OKUDA, Tatsuya SUZUKI, "Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 10, pp. 2095-2105, October 2017, doi: 10.1587/transfun.E100.A.2095.
Abstract: This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2095/_p
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@ARTICLE{e100-a_10_2095,
author={Thomas WILHELEM, Hiroyuki OKUDA, Tatsuya SUZUKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior},
year={2017},
volume={E100-A},
number={10},
pages={2095-2105},
abstract={This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.},
keywords={},
doi={10.1587/transfun.E100.A.2095},
ISSN={1745-1337},
month={October},}
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TY - JOUR
TI - Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2095
EP - 2105
AU - Thomas WILHELEM
AU - Hiroyuki OKUDA
AU - Tatsuya SUZUKI
PY - 2017
DO - 10.1587/transfun.E100.A.2095
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2017
AB - This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.
ER -
English
