Input selection in N2SID using group lasso regularization

Klingspor, Måns; Hansson, Anders; Löfberg, Johan; Verhaegen, Michel

doi:10.1016/j.ifacol.2017.08.1472

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2019

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“…In the case of an excessive amount of inputs that do not affect the system and noise present in the measurements, performing so called input selection can yield a better model [15]. For instance, suppose that we have a MISO system with input signal u 1 (t), ..., u p (t) and a scalar output signal y(t) ∈ R. it is thus desirable to achieve zero columns to remove redundant inputs.…”

Section: Input Selectionmentioning

confidence: 99%

Low-rank optimization in system identification

Klingspor

2019

Linköping Studies in Science and Technology. Licentiate Thesis

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This is a Swedish Licentiate's Thesis. Swedish postgraduate education leads to a Doctor's degree and/or a Licentiate's degree. A Doctor's Degree comprises 240 ECTS credits (4 years of full-time studies). A Licentiate's degree comprises 120 ECTS credits, of which at least 60 ECTS credits constitute a Licentiate's thesis. Linköping studies in science and technology. Licentiate Thesis No. 1855 Low-rank optimization in system identification:

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Section: Input Selectionmentioning

confidence: 99%