Accelerating Parameterizaton of Large-Scale Dynamic Models Through the Use of Activity Analysis
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Previous research presents both sensitivity-based and principal component-based techniques for improving the tractability of system identification. Both have proven viable, but the former may be computationally inefficient for large problems, and the latter require a change of realization that may compromise the physical meaning of the parameters to be identified. This paper proposes for the first time the use of activity analysis, an efficient and realization-preserving model reduction technique, for identification space reduction. Theoretical and numerical studies highlighting the viability of activity analysis versus the previous two methods are presented.
2006 ◽
2018 ◽
Vol 332
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pp. 363-381
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2020 ◽
Vol 498
(4)
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pp. 5916-5935
2019 ◽
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2021 ◽
Vol 503
(1)
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pp. 270-291
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2020 ◽
Vol 501
(1)
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pp. L71-L75
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