scholarly journals Identification of the platform with electric drive using a differentiating filters

2018 ◽  
Vol 19 (6) ◽  
pp. 375-379
Author(s):  
Leszek Cedro ◽  
Krzysztof Wieczorkowski
Keyword(s):  
Parameter Identification ◽  
Degrees Of Freedom ◽  
Identification Problem ◽  
Least Square ◽  
Identification Algorithm ◽  
Measurement Results ◽  
Parameter Identification Problem ◽  
Derivatives Of ◽  
Linear Parameterization ◽  
Derivative Order

The paper presents an example of solving the parameter identification problem in case of platform with one degrees of freedom has been also presented. The parameter identification algorithm based on linear parameterization of the platform model and the least square criteria is developed. The desired derivatives of measured signals are estimated by means of designed differentiation filters. The required derivative order depends on the order of differential equations describing the object. The model was identified and verified using measurement results obtained for a real system.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals Multilayer method for solving a problem of metals rupture under creep conditions

2019 ◽  
Vol 23 (Suppl. 2) ◽  
pp. 575-582 ◽  
Author(s):  
Evgenii Kuznetsov ◽  
Sergey Leonov ◽  
Dmitry Tarkhov ◽  
Alexander Vasilyev
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Parameter Identification ◽  
Uniaxial Tension ◽  
Network Modeling ◽  
Identification Problem ◽  
Neural Network Modeling ◽  
Creep Theory ◽  
Parameter Identification Problem ◽  
Kinetic Creep

The paper deals with a parameter identification problem for creep and fracture model. The system of ordinary differential equations of kinetic creep theory is applied for describing this model. As for solving the parameter identification problem, we proposed to use the technique of neural network modeling, as well as the multilayer approach. The procedures of neural network modeling and multilayer approximation constructing application is demonstrated by the example of finding parameters for uniaxial tension model for isotropic steel 45 specimens at creep conditions. The solution corresponding to the obtained parameters agrees well with theoretical strain-damage characteristics, experimental data, and results of other authors.

The parameter identification problem for the somatic shunt model

1992 ◽  
Vol 66 (4) ◽  
pp. 307-318 ◽  
Author(s):  
John A. White ◽  
Paul B. Manis ◽  
Eric D. Young
Keyword(s):  
Parameter Identification ◽  
Identification Problem ◽  
Parameter Identification Problem
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