A General Procedure for Accurate Parameter Estimation in Dynamic Systems Using New Estimation Errors

2012 ◽  
pp. 149-166
Author(s):  
Masahiko Nakatsui ◽  
Alexandre Sedoglavic ◽  
François Lemaire ◽  
François Boulier ◽  
Asli Ürgüplü ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Dynamic Systems ◽  
General Procedure ◽  
Estimation Errors

Model Verification of Mixed Dynamic Systems

1978 ◽  
Vol 100 (2) ◽  
pp. 266-273 ◽  
Author(s):  
J. D. Chrostowski ◽  
D. A. Evensen ◽  
T. K. Hasselman
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Dynamic Systems ◽  
Statistical Parameter ◽  
Model Verification ◽  
Passive Networks ◽  
Parameter Values ◽  
Basic Configuration ◽  
General Method

A general method is presented for using experimental data to verify math models of “mixed” dynamic systems. The term “mixed” is used to suggest applicability to combined systems which may include interactive mechanical, hydraulic, electrical, and conceivably other types of components. Automatic matrix generating procedures are employed to facilitate the modeling of passive networks (e.g., hydraulic, electrical). These procedures are augmented by direct matrix input which can be used to complement the network model. The problem of model verification is treated in two parts; verification of the basic configuration of the model and determination of the parameter values associated with that configuration are addressed sequentially. Statistical parameter estimation is employed to identify selected parameter values, recognizing varying degrees of uncertainty with regard to both experimental data and analytical results. An example problem, involving a coupled hydraulic-mechanical system, is included to demonstrate application of the method.

Practical Implications of Modeling Commercial Vehicle Empty Trips

2003 ◽  
Vol 1833 (1) ◽  
pp. 87-94 ◽  
Author(s):  
José Holguin-Veras ◽  
Ellen Thorson
Keyword(s):  
Parameter Estimation ◽  
Commercial Vehicle ◽  
Complex Case ◽  
Statistical Distributions ◽  
Estimation Errors ◽  
Theoretical Derivation ◽  
Basic Model ◽  
Order Of Magnitude ◽  
Practical Implications ◽  
Better Than

Implications of modeling commercial vehicle empty trips are discussed, a theoretical derivation for parameter estimation is provided, and insight is given into the order of magnitude of estimation errors because of the improper modeling of commercial vehicle empty trips. A set of relatively simple cases was designed to illustrate the most important implications. Also addressed are estimation errors from using naïve approaches to compensate for the lack of explicit modeling of empty trips and the errors associated with more advanced empty trip models. In the simplest simulation, directional errors for a basic complementary model were from three to six times fewer than those for the naïve models. In the more complex case, a more sophisticated complementary model performed slightly better than the basic model and both complementary models were considerably better than the naïve approaches. The directional errors for the naïve models were four to seven times greater than those for the complementary models. Moreover, an analysis of the statistical distributions of the errors indicated that the complementary models had higher probabilities of producing accurate results, whereas the naïve approaches had higher probabilities of producing very large errors. These analyses indicate that the naïve approaches translate into significant errors in directional-traffic estimates. For that reason, their use should be discontinued in favor of the more advanced models presented.

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