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On the optimal continuous experimental design problem

100%

Kitsos C.

Discussiones Mathematicae Probability and Statistics

2011

tom 31

nr 1-2

59-70

The target of this paper is to provide a compact review of the Optimal Experimental Design, the continuous case. Therefore we are referring to the general nonlinear problem in comparison to the linear one.

Application of the concurrent engineering in the machine tool design.

63%

Biedunkiewicz W. , Kuligowski G.

Postępy Technologii Maszyn i Urządzeń

1999

tom Vol. 23, nr 3

5-15

Article presents general methodology of the machine tool concurrent design. Concurrent design is compared with the traditional sequential design-manufacturing process. Article presents the example of the process of the machine tool concurrent design process.

W pracy przedsatwiono ogólną metodykę projektowania współbieżnego obrabiarek. Zestawiono założenia i porównano metodę projektowania współbieżnego z tradycyjnym sekwencyjnym procesem projektowo-wytwórczym. Zamieszczono przykład procesu projektowania współbieżnego fragmentu konstrukcji obrabiarki.

Adaptive control for sequential design

63%

Gautier R. , Pronzato L.

Discussiones Mathematicae Probability and Statistics

2000

tom 20

nr 1

97-114

The optimal experiment for estimating the parameters of a nonlinear regression model usually depends on the value of these parameters, hence the problem of designing experiments that are robust with respect to parameter uncertainty. Sequential designpermits to adapt the experiment to the value of the parameters, and can thus be considered as a robust design procedure. By designing theexperiments sequentially, one introduces a feedback of information, and thus dynamics, into the design procedure. Several sequential schemes, corresponding to different control policies, are considered. The optimal one corresponds to closed-loop control, and is solution of a stochastic dynamic-programming problem, which is extremely difficult to solve. A suboptimal strategy is proposed, which relies ona normal approximation of the future posterior of θ, independent of future observations. The design criterion obtained involves several mathematical expectations, which are approximated by Laplace method. Finally, stochastic approximation algorithms are also suggested to determine (sub)optimal sequential experiments without having to compute expectations.