Derivative-free Parameter Optimization of Functional Mock-up Units
(2012) 9th International Modelica Conference- Abstract
- Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the parameter values of the system. The parameters can sometimes be measured or calculated, however some of them are often difficult or impossible to obtain in these ways. Finding accurate parameter values is crucial for the accuracy of the mathematical model.
Estimating the parameters using optimization algorithms which attempt to
minimize the error between the response from the mathematical model and the physical system is a common approach for improving the accuracy of the model.
Optimization algorithms usually requires information about the... (More) - Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the parameter values of the system. The parameters can sometimes be measured or calculated, however some of them are often difficult or impossible to obtain in these ways. Finding accurate parameter values is crucial for the accuracy of the mathematical model.
Estimating the parameters using optimization algorithms which attempt to
minimize the error between the response from the mathematical model and the physical system is a common approach for improving the accuracy of the model.
Optimization algorithms usually requires information about the derivatives which may not always be available or not be appropriate for estimation which forces the use of derivative-free optimization algorithms.
In this paper, we present an implementation of derivative-free optimization algorithms for parameter estimation in the JModelica.org platform. The implementation allows the underlying dynamic system to be represented as a Functional Mock-up Unit (FMU), thus enables parameter estimation of models designed in modeling tools following the standardized interface, the Functional Mock-up Interface (FMI), such as Dymola. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2972270
- author
- Andersson, Christian LU ; Gedda, Sofia ; Åkesson, Johan LU and Diehl, Stefan LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Derivative-free optimization Parameter Estimation JModelica.org FMI Assimulo
- host publication
- [Host publication title missing]
- publisher
- Modelica Association
- conference name
- 9th International Modelica Conference
- conference location
- Munich, Germany
- conference dates
- 2012-09-03
- project
- LCCC
- language
- English
- LU publication?
- yes
- additional info
- key=and+12mc project=LCCC-modeling
- id
- 2ff1c8c5-3927-4a65-a994-8200558c6e97 (old id 2972270)
- date added to LUP
- 2016-04-04 10:08:51
- date last changed
- 2018-11-21 20:57:02
@inproceedings{2ff1c8c5-3927-4a65-a994-8200558c6e97,
abstract = {{Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the parameter values of the system. The parameters can sometimes be measured or calculated, however some of them are often difficult or impossible to obtain in these ways. Finding accurate parameter values is crucial for the accuracy of the mathematical model.<br/><br>
<br/><br>
Estimating the parameters using optimization algorithms which attempt to<br/><br>
minimize the error between the response from the mathematical model and the physical system is a common approach for improving the accuracy of the model.<br/><br>
<br/><br>
Optimization algorithms usually requires information about the derivatives which may not always be available or not be appropriate for estimation which forces the use of derivative-free optimization algorithms.<br/><br>
<br/><br>
In this paper, we present an implementation of derivative-free optimization algorithms for parameter estimation in the JModelica.org platform. The implementation allows the underlying dynamic system to be represented as a Functional Mock-up Unit (FMU), thus enables parameter estimation of models designed in modeling tools following the standardized interface, the Functional Mock-up Interface (FMI), such as Dymola.}},
author = {{Andersson, Christian and Gedda, Sofia and Åkesson, Johan and Diehl, Stefan}},
booktitle = {{[Host publication title missing]}},
keywords = {{Derivative-free optimization Parameter Estimation JModelica.org FMI Assimulo}},
language = {{eng}},
publisher = {{Modelica Association}},
title = {{Derivative-free Parameter Optimization of Functional Mock-up Units}},
url = {{https://lup.lub.lu.se/search/files/5472603/2972271.pdf}},
year = {{2012}},
}