Method and system for on-line system identification

1997 ◽  
Vol 102 (5) ◽  
pp. 2479
Author(s):  
Graham P. Eatwell
Keyword(s):  
System Identification ◽  
Line System ◽  
On Line

Analog methods for on-line system identification using noisy measurements

SIMULATION ◽  
1968 ◽  
Vol 11 (5) ◽  
pp. 241-248 ◽  
Author(s):  
D.W. Ricker ◽  
G.N. Saridis
Keyword(s):  
System Identification ◽  
Nonlinear Systems ◽  
Automatic Control ◽  
Stochastic Approximation ◽  
Linear Time ◽  
Measurement Noise ◽  
Current Interest ◽  
Line System ◽  
On Line ◽  
Noisy Measurements

Of current interest in the field of automatic control is the problem of system identification in the presence of measurement noise. Generally this problem has been dis cussed in the literature for the case of linear time-invar iant systems where the parameters to be identified are constant or slowly varying. This paper describes the ap plication of continuous stochastic approximation meth ods for the identification of a class of simple nonlinear systems. The two algorithms described are easily imple mented with analog equipment, although one of them requires some logic capability.

On-line system identification with an adjustable estimation interval

1988 ◽  
Vol 19 (10) ◽  
pp. 1955-1967 ◽  
Author(s):  
WEN-TENG WU ◽  
WEI-HSIUNG OU ◽  
KUO-CHIEH CHEN
Keyword(s):  
System Identification ◽  
Line System ◽  
On Line ◽  
Estimation Interval

Validity Evaluations of On-Line Driver Modeling and State Assessment

2007 ◽  
Author(s):  
Liang-Kuang Chen ◽  
Meng-Hsuan Peng
Keyword(s):  
System Identification ◽  
Driving Simulator ◽  
State Assessment ◽  
Model Parameters ◽  
Steering Control ◽  
Line System ◽  
Validity Indices ◽  
On Line ◽  
Identification Techniques ◽  
Line Driver

Although driver steering control models and on-line model identification have been studied extensively, the application of the driver models to driver state assessment is seldom investigated. Furthermore, the validity level, or confidence index, of the on-line modeling and assessment of driver behavior is not reported in the literature. In this paper, on-line system identification techniques are applied to the determination of driver model parameters and model validity estimation. The driver steering control model is estimated on-line using system identification techniques. The on-line driver state assessment is achieved using probabilistic neural network (PNN), and the validity of the assessment is derived from the likelihood function inside the PNN. Preliminary results show that the computed validity indices agree with expectation reasonably well. More driving simulator experiments will be conducted to validate the proposed indices.

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