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Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

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H. J. Dreef, S. Shi, X. Cheng, M. C. F. Donkers, P. M. J. Van den Hof

Systems and Control · Submitted 22 Jan 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals.

how it is stated · asserted 1.00 read the record →

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arXiv ID2201.09095 (version 2)
Pith ID3CIRZIMR, Pith's permanent record of this paper and its review
CitationH. J. Dreef, M. C. F. Donkers, P. M. J. Van den Hof, S. Shi, X. Cheng. "Excitation allocation for generic identifiability of linear dynamic networks with fixed modules." arXiv:2201.09095 (2022). https://pith.science/paper/3CIRZIMR
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,179 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.82 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. background 1.00 0.07 0.04 asserted 1.00
s1 The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. background 1.00 0.03 0.03 asserted 1.00
s2 Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. contribution 0.98 0.67 0.07 asserted 0.91
s3 The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. result 0.93 0.88 0.74 asserted 1.00
s4 An algorithm is presented that allocates the external signals in a systematic fashion. method 0.50 0.93 0.09 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.86 · evidence from proof or derivation 0.94.

What was examined, and what was not

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  • full text
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  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.

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