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Parameter Estimation of Nonlinear Response Surface Models by Using Genetic Algorithm and Unscented Kalman Filter

  • Özlem TürkşenEmail author
  • Esin Köksal Babacan
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Some of the real world problems are characterized by using nonlinear functions in the parameters. In this case, optimization of nonlinear response surface models become challenging with derivative-based optimization methods. In this study, two of the derivative free methods, Genetic Algorithm (GA) and Unscented Kalman Filter (UKF), are used for parameter estimation of complex nonlinear response surface model. A numerical example in chemical science is given to illustrate the performance of the methods.

Keywords

Nonlinear response problems Parameter estimation Genetic algorithm Unscented Kalman filter 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Science, Statistics DepartmentAnkara UniversityAnkaraTurkey

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