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Parameter identification based on FE‐models by genetic algorithms
Author(s) -
Bernstein S.,
Riedel J.
Publication year - 2002
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/1617-7061(200203)1:1<238::aid-pamm238>3.0.co;2-3
Subject(s) - precondition , identification (biology) , differentiable function , genetic algorithm , finite element method , mathematical optimization , algorithm , regular polygon , function (biology) , computer science , convex optimization , estimation theory , system identification , meta optimization , mathematics , structural engineering , engineering , data mining , mathematical analysis , geometry , botany , evolutionary biology , biology , programming language , measure (data warehouse)
A realistic and reliable model is an important precondition for the simulation of revitalization tasks as well as for the estimation of properties of existing buildings. Within one theory the parameters of the model should be approximated best by gradually performed experiments and their analysis. Usually this kind of optimization problems leads into non‐convex non‐differentiable objective function spaces with high dimensions. Normally ore complex structures are modeled using finite element method. We present a method of identifying Young's modulus for a beam and a plate by using FE‐models and genetic optimization algorithms for parameter identification.