scholarly journals Parameter identification algorithm for convection-diffusion transport model

2021 ◽  
Vol 1745 (1) ◽  
pp. 012110
Author(s):  
A N Kuvshinova ◽  
A V Tsyganov ◽  
Yu V Tsyganova ◽  
H R Tapia Garza
Keyword(s):  
Parameter Identification ◽  
Transport Model ◽  
Identification Algorithm ◽  
Diffusion Transport ◽  
Convection Diffusion

scholarly journals Dynamic identification of boundary conditions for convection-diffusion transport model in the case of noisy measurements

2019 ◽  
Vol 21 (4) ◽  
pp. 469-479
Author(s):  
Anastasia N. Kuvshinova
Keyword(s):  
Boundary Conditions ◽  
Transport Model ◽  
Mixed Boundary ◽  
Mixed Boundary Conditions ◽  
Simultaneous Estimation ◽  
Dynamic Identification ◽  
Diffusion Transport ◽  
One Dimensional ◽  
Convection Diffusion ◽  
Noisy Measurements

The paper addresses the problem of dynamic identification of mixed boundary conditions for one-dimensional convection-diffusion transport model based on noisy measurements of the function of interest. Using finite difference method the original model with the partial differential equation is replaced with the discrete linear dynamic system with noisy multisensor measurements in which boundary conditions are included as unknown input vector. To solve the problem, the algorithm of simultaneous estimation of the state and input vectors is used. The results of numerical experiments are presented which confirm the practical applicability of the proposed method.

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 Physical Parameter Identification Algorithm Modeling for Complex Microsystem via Triangulation Functions

2018 ◽  
Vol 7 (4.36) ◽  
pp. 962
Author(s):  
Elena N. Meshcheryakova ◽  
. .
Keyword(s):  
Parameter Identification ◽  
Physical Parameter ◽  
Delaunay Triangulation ◽  
Model Development ◽  
Physical Object ◽  
Identification Algorithm ◽  
Classification Analysis ◽  
Advantages And Disadvantages ◽  
Object Parameters ◽  
D Model

This article describes the possibility of triangulation function use for the classification, analysis and identification of complex microsystem physical object parameters. They analyzed the existing methods and identification algorithms, their advantages and disadvantages are highlighted. The existing methods of triangulation are considered, the possibility of Delaunay triangulation is described for surfactant signal 3-D model development and analysis. They developed the algorithm to identify the state of an object using the triangulation function that takes into account the change of node coordinates and the length of the triangulation grid edges. They presented the visual UML model. The conclusions are drawn about the possibility of triangulation function use for the analysis of complex microsystem state.  

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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