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In this paper, application-oriented experiment design formulated as a chance constrained problem is investigated. The chance constraint is based on the presumption that the estimated model can be used in an application to achieve a given performance level with a prescribed probability. The aforementioned performance level is dictated by the particular application of interest. The resulting optimization...
A key problem in optimal input design is that the optimal input depends on some unknown system parameters that are to be identified. Adaptive design is one of the fundamental routes to handle this problem. This paper proposes an adaptive input design method for ARMAX systems based on the general stochastic framework outlined in the reference [10].
A particular cascade structure system identification problem is formulated for the purpose of characterising the vacuum-plasma response for a magnetic confinement fusion experiment. A predictor-form closed-loop subspace system identification approach is advocated due to (i) plant instability (ii) sizes of input-output vectors and (iii) inherent multivariate eigenmodes of the physical system. Since...
We analyze and compare methods to estimate the ℒ2-gain (ℋ∞-norm) of a stable linear dynamical system, that is the maximum of the absolute value of the corresponding frequency response. The standard approach is to estimate a parametric model of the system, which then is used for gain calculation. An asymptotic error variance expression for ℋ∞-norm estimates based on finite impulse response (FIR) models...
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