Robust parameter estimation method for bilinear model

2015 ◽  
Author(s):  
Mohd Isfahani Ismail ◽  
Hazlina Ali ◽  
Sharipah Soaad S. Yahaya
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Bilinear Model ◽  
Parameter Estimation Method ◽  
Robust Parameter ◽  
Robust Parameter Estimation

scholarly journals A Robust Parameter Estimation Method for Estimating Disease Burden of Respiratory Viruses

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e90126 ◽  
Author(s):  
King Pan Chan ◽  
Chit Ming Wong ◽  
Susan S. S. Chiu ◽  
Kwok Hung Chan ◽  
Xi Ling Wang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Disease Burden ◽  
Estimation Method ◽  
Respiratory Viruses ◽  
Parameter Estimation Method ◽  
Robust Parameter ◽  
Robust Parameter Estimation

Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and its Application to Heavy Duty Vehicles

2005 ◽  
Vol 128 (3) ◽  
pp. 523-531 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan Kulakowski
Keyword(s):  
Parameter Estimation ◽  
Experimental Validation ◽  
Hybrid Genetic Algorithm ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Estimation Technique ◽  
Analysis Experiment ◽  
Input Signals ◽  
Robust Parameter ◽  
Robust Parameter Estimation

In this paper, hybrid parameter estimation technique is developed to improve computational efficiency and accuracy of pure GA-based estimation. The proposed strategy integrates a GA and the Maximum Likelihood Estimation. Choices of input signals and estimation criterion are discussed involving an extensive sensitivity analysis. Experiment-related aspects, such as the imperfection of data acquisition, are also considered. Computer simulation results reveal that the hybrid parameter estimation method proposed in this study is very efficient and clearly outperforms conventional techniques and pure GAs in accuracy, efficiency, as well as robustness with respect to the initial guesses and measurement uncertainty. Primary experimental validation is also implemented, including the interpretation of field test data, as well as analysis of errors associated with aspects of experiment design.

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