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This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control.
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The objective of this contribution is to quantify benefits of optimal input design compared to the use of standard identification input signals, e.g. PRBS signals for some common, and important, application areas of system identification.
We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design.
The practical value of proposed robust algorithm for estimation of OE model parameters with constrained output variance is further increased by using an optimal input design.
The quite relevant improvement obtained using optimal input sequences instead of random sequences is shown by simulation.
Optimal use of many farm inputs can be achieved if the size of holding is a big one.
The architecture selection algorithm is completed with a structure identification procedure, used to obtain the optimal input space partitioning of the different sub-grids of the model.
Design of an optimal input signal in system identification using a multi-layer perceptron network is treated.
Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level.
Finally, we estimated the GWQI values of the all pixels using the validated fuzzy network and the optimal inputs.
The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design.
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