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ndltd-NEU--neu-rx915q09c2021-05-28T05:22:17ZSparsity based methods in system identificationMany practical situations involve synthesizing controllers for systems where a priori models are not available and thus must be identified from experimental data. In most cases, it is possible to find multiple models that are consistent with the data. It is of interest to identify a simpler model compatible with the available information whenever possible than a more complex one. One of the motivations is the fact that the order of the plant model is reflected in the order of the resulting controllers. Another reason is the simplicity of analysis with low order models.http://hdl.handle.net/2047/D20194326
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Many practical situations involve synthesizing controllers for systems where a priori models are not available and thus must be identified from experimental data. In most cases, it is possible to find multiple models that are consistent with the data. It is of interest to identify a simpler model compatible with the available information whenever possible than a more complex one. One of the motivations is the fact that the order of the plant model is reflected in the order
of the resulting controllers. Another reason is the simplicity of analysis with low order models.
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Sparsity based methods in system identification
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Sparsity based methods in system identification
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Sparsity based methods in system identification
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Sparsity based methods in system identification
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Sparsity based methods in system identification
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Sparsity based methods in system identification
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sparsity based methods in system identification
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http://hdl.handle.net/2047/D20194326
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1719407946946838528
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