NDLI: Influence of objective function on parameter identification result

Content Provider	IEEE Xplore Digital Library
Author	Jie Qu Guangsan Huang
Copyright Year	2011
Description	Author affiliation: College of Automotive Engineering, South China University of Technology, Guangzhou, 510641, China (Jie Qu; Guangsan Huang)
Abstract	With the development of the computer technology and the numerical technology, inverse analysis method has been widely applied to identify the mode parameter. The parameter identification methodology mainly includes the selection of the appropriate experimental data, the definition of the objective function and the optimization algorithm. However, little study has been reported on how to define the appropriate objective function for the goodness-of-fit of a model to a set of experimental data till now. Taking the parameter identification of a macro-micro coupled superplastic constitutive model as example, the influence of the selected objective function form on the parameter identification result is studied. The selected objective function form includes hetereoscedastic maximum likelihood error estimator (HMLE) and improved mean squared-error estimator (S-RMSE), which the data of every sample is normalized with the according average value. The study shows that the selection of the objective function has important influence on the parameter identification result. For the identification of the macro-micro coupled superplastic constitutive model, the identification result is better, when the S-RMSE is adopted. It may be due to that it couple the strength of HMLE and that of MSE.
Starting Page	2274
Ending Page	2277
File Size	253601
Page Count	4
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5777159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Computational modeling Optimization methods Hybrid optimization method Goodness-offit Materials Modelling Numerical models Parameter identification Strain Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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