Frequency domain global optimization algorithm for the aircraft flutter model parameter identification

2010 ◽  
Author(s):  
Jianhong Wang ◽  
Daobo Wang ◽  
Zhisheng Wang
Keyword(s):  
Global Optimization ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Optimization Algorithm ◽  
Global Optimization Algorithm ◽  
Model Parameter ◽  
Model Parameter Identification

scholarly journals SOFC model parameter identification by means of Modified African Vulture Optimization algorithm

2021 ◽  
Vol 7 ◽  
pp. 7251-7260
Author(s):  
Hamid Asadi Bagal ◽  
Yashar Nouri Soltanabad ◽  
Milad Dadjuo ◽  
Karzan Wakil ◽  
Mansoureh Zare ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Optimization Algorithm ◽  
Model Parameter ◽  
Model Parameter Identification

scholarly journals PEM fuel cells model parameter identification based on a new improved fluid search optimization algorithm

2020 ◽  
Vol 6 ◽  
pp. 813-823 ◽  
Author(s):  
Yan Cao ◽  
Xiaoxi Kou ◽  
Yujia Wu ◽  
Kittisak Jermsittiparsert ◽  
Abdullah Yildizbasi
Keyword(s):  
Fuel Cells ◽  
Parameter Identification ◽  
Optimization Algorithm ◽  
Pem Fuel Cells ◽  
Model Parameter ◽  
Search Optimization ◽  
Model Parameter Identification

Optimization Algorithm Comparison for In-Plane Flexible Ring Tire Model Parameter Identification

2016 ◽  
Author(s):  
Bin Li ◽  
Xiaobo Yang ◽  
James Yang ◽  
Yunqing Zhang ◽  
Zeyu Ma
Keyword(s):  
Parameter Identification ◽  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Optimization Algorithms ◽  
Tire Model ◽  
Model Parameter ◽  
Algorithm Comparison ◽  
Huge Impact ◽  
Model Parameter Identification ◽  
Flexible Ring

The parameter values used in a tire model directly determine the prediction accuracy of the model. Poorly identified parameters lead to incorrect prediction of tire performances. The optimization algorithm used for parameter identification has a huge impact on the quality of the identified parameters for a tire model. In this paper, four different optimization algorithms in MATLAB, including local optimization algorithms (fminsearchcon and patternsearch) and global optimization algorithms (particleswarm and GA-genetic algorithm), are applied to identify the parameters of a newly proposed in-plane flexible ring tire model based on one cleat experiment results, respectively. Their performances are compared in terms of the prediction accuracy, efficiency and some other aspects. After the comparison, the most suitable optimization algorithm for tire model parameter identification is obtained. Finally, the parameters that are identified based on the set of parameters from the most suitable algorithm are used to predict the other cleat test conditions to further validate the tire model.

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