Abstract
In this paper, we propose a new model set identification method using periodgrams obtained from experimental data. One of the difficulties in the existing model set identification methods is that identified model sets are conservative, since they adopt the unknown-but-bounded noise sets which ignore empirical noise properties. In the proposed method, we consider the unknown but bounded noise set of cross-periodgrams of input and noise. This noise set is prescribed such that the upper bound of cross-periodgrams gets smaller by increasing the data number because of the low correlation properties of noise. As a result, the noise effect in model set identification is decreased by increasing the data number. Also, since this noise set is convex, the identification problem is reduced to a convey optimization problem. Numerical exumples show the effectiveness of the proposed method.
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