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Description
  • the parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model of the problem is often multimodal in nature and requires a suitable global optimization method for its solution. In the present study we show the application of a Modified Differential Evolution (MDE) for solving parameter estimation problem. We have considered two test cases. A comparison of numerical results with other algorithms shows the competence of MDE over basic DE and other methods.
  • the parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model of the problem is often multimodal in nature and requires a suitable global optimization method for its solution. In the present study we show the application of a Modified Differential Evolution (MDE) for solving parameter estimation problem. We have considered two test cases. A comparison of numerical results with other algorithms shows the competence of MDE over basic DE and other methods. (en)
Title
  • Modified Differential Evolution Algorithm for Parameter Estimation in Mathematical Models
  • Modified Differential Evolution Algorithm for Parameter Estimation in Mathematical Models (en)
skos:prefLabel
  • Modified Differential Evolution Algorithm for Parameter Estimation in Mathematical Models
  • Modified Differential Evolution Algorithm for Parameter Estimation in Mathematical Models (en)
skos:notation
  • RIV/61989100:27240/10:86081158!RIV12-MSM-27240___
http://linked.open...avai/riv/aktivita
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  • Z(MSM6198910027)
http://linked.open...vai/riv/dodaniDat
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  • 272246
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  • RIV/61989100:27240/10:86081158
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  • opposition-hased learning; mathematical models; parameter estimation; Differential Evolution (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F7FA33D7D9FA]
http://linked.open...v/mistoKonaniAkce
  • Istanbul
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  • 345 E 47TH ST, NEW YORK, NY 10017 USA
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  • IEEE International Conference on Systems, Man, and Cybernetics, SMC 2010 : conference proceedings
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  • Abraham Padath, Ajith
  • Snášel, Václav
  • Ali, Musrrat
  • Pant, Millie
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http://linked.open...ain/vavai/riv/wos
  • 000295015302055
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 1062-922X
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/ICSMC.2010.5641794
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  • IEEE
https://schema.org/isbn
  • 978-1-4244-6588-0
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  • 27240
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