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 require information about the derivatives which may not always be easily available or which may be difficult to compute due to; e.g.; hybrid dynamics. In such cases; derivative-free optimization algorithms offer an alternative for design and parameter optimization.
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.
Keywords: Derivative-free optimization; Parameter Estimation; JModelica.org; FMI; Assimulo
Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany
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