Non-linear model reduction by genetic algorithms with using a system structure related fitness function

Buttelmann, Maik; Lohmann, Boris

doi:10.23919/ecc.2001.7076194

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Methods For Simplifying the Reconstruction Matrix W Nc1

Introduction1

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2004

2009

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Cited by 4 publications

(2 citation statements)

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“…In this GA, a gene is a binary string of length n, where each bit indicates, whether the corresponding element of w T j should be enforced to zero or not. For more details see [3,4,5] and section 6.4.…”

Section: Methods For Simplifying the Reconstruction Matrix W Ncmentioning

confidence: 99%

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A Compromise Between Simplicity and Accuracy of Nonlinear Reduced Order Systems

Yousefi∗¹,

Lohmann²

2009

International Journal of Modelling and Simulation

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This report considers the problem of complexity in reduced nonlinear models derived by the so-called system matrices optimization method. The solution suggested here is adding a class of secondary conditions that impose a simpler structure on the reduced model. Distinct choices of these conditions have different impacts on the accuracy of the reduced model, therefore a pioneered search based on genetic algorithms is proposed that finds the optimal choice of conditions. By means of two examples, it is illustrated that this method can effectively compromise between simplicity and accuracy of the reduced model. In order to improve the numerical efficiency of the solution and to speed up the pace of convergence some enhancements are presented.

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Section: Methods For Simplifying the Reconstruction Matrix W Ncmentioning

confidence: 99%

“…The idea behind structure simplification is simplifying the relations and coupling among the state variables. This idea is published in [3] and it was developed later in [4,5,29] and [28].…”

Section: Introductionmentioning

confidence: 99%