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- W2783271780 abstract "In this paper, we consider the problem of set-membership identification of multiple-input multiple-output (MIMO) linear models when both input and output measurements are affected by bounded additive noise. Firstly, we propose a general formulation that allows the user to take into account possible a-priori information on the structure of the MIMO model to be identified. Then, we formulate the problem in terms of a suitable polynomial optimization problem that is solved by means of a convex relaxation approach. To show the effectiveness of the proposed approach, we test the original MIMO identification algorithm on a simulation example, as well as on a set of input–output experimental data, collected on a multiple-input multiple-output electronic process simulator." @default.
- W2783271780 created "2018-01-26" @default.
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- W2783271780 date "2018-04-01" @default.
- W2783271780 modified "2023-10-02" @default.
- W2783271780 title "Set-membership errors-in-variables identification of MIMO linear systems" @default.
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- W2783271780 doi "https://doi.org/10.1016/j.automatica.2017.12.042" @default.
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