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A modified form of Littlewood Paley (L P) basis function is used for the identification of the parameters.
This paper describes a method for the identification of the parameters entering into the equations of motion of distributed systems.
This paper introduces a method for the identification of the parameters of nonlinear structures using a probabilistic Bayesian framework, employing a Markov chain Monte Carlo algorithm.
An original experimental test for the identification of the parameters of the short and long time behaviours of bent pultruded tubes is then detailed.
A model-based experiment design techniques is used to design improved test protocols for the identification of the parameters of a detailed physiological models of diabetes specific to single subjects.
Finally, a maximum likelihood framework is proposed for the identification of the parameters associated with the uncertainty level and the mean model, or part thereof, using either natural frequencies only or natural frequencies and mode shape information of the beams around their final clamped clamped state.
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The procedure for the identification of the material parameters is outlined.
A procedure for the identification of the strength parameters is outlined.
The procedure adopted for the identification of the VCB model parameters is described.
Using combined objective functions is beneficial especially for the identification of the soil routine parameters.
The parameter identification procedure used for the identification of the ElastoPlastic friction model parameters is explained in detail.
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