On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm
Summary :
This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.
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- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.9 pp.2372-2378
- Publication Date
- 2004/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
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Tomohiro HACHINO, Hitoshi TAKATA, "On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 9, pp. 2372-2378, September 2004, doi: .
Abstract: This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_9_2372/_p
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@ARTICLE{e87-a_9_2372,
author={Tomohiro HACHINO, Hitoshi TAKATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm},
year={2004},
volume={E87-A},
number={9},
pages={2372-2378},
abstract={This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2372
EP - 2378
AU - Tomohiro HACHINO
AU - Hitoshi TAKATA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2004
AB - This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.
ER -
English
