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Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method

Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method

Marwa Elhajj, Rafic Younes, Sebastien Charles, Eric Padiolleau
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 28
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466652415|DOI: 10.4018/ijaec.2014070102
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MLA

Elhajj, Marwa, et al. "Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method." IJAEC vol.5, no.3 2014: pp.14-41. http://doi.org/10.4018/ijaec.2014070102

APA

Elhajj, M., Younes, R., Charles, S., & Padiolleau, E. (2014). Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method. International Journal of Applied Evolutionary Computation (IJAEC), 5(3), 14-41. http://doi.org/10.4018/ijaec.2014070102

Chicago

Elhajj, Marwa, et al. "Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method," International Journal of Applied Evolutionary Computation (IJAEC) 5, no.3: 14-41. http://doi.org/10.4018/ijaec.2014070102

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Abstract

The calibration of the model is one of the most important steps in the development of models of engineering systems. A new approach is presented in this study to calibrate a complex multi-domain system. This approach respects the real characteristics of the circuit, the accuracy of the results, and minimizes the cost of the experimental phase. This paper proposes a complete method, the Global Optimization Method for Parameter Calibration (GOMPC). This method uses an optimization technique coupled with the simulated model on simulation software. In this paper, two optimization techniques, the Genetic Algorithm (GA) and the two-level Genetic Algorithm, are applied and then compared on two case studies: a theoretical and a real hydro-electromechanical circuit. In order to optimize the number of measured outputs, a sensitivity analysis is used to identify the objective function (OBJ) of the two studied optimization techniques. Finally, results concluded that applying GOMPC by combining the two-level GA with the simulated model was an efficient solution as it proves its accuracy and efficiency with less computation time. It is believed that this approach is able to converge to the expected results and to find the system's unknown parameters faster and with more accuracy than GA.

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