2019
DOI: 10.1109/tac.2019.2894369
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Comprehending Complexity: Data-Rate Constraints in Large-Scale Networks

Abstract: DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal re… Show more

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Cited by 13 publications
(9 citation statements)
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References 34 publications
(47 reference statements)
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“…Extensions of this result for stabilizing nonlinear systems in the vicinity of the origin and observing nonlinear systems through finite capacity communication channels, including large networks, were obtained in the series of the subsequent papers, see [58][59][60][61][62] to mention a few.…”
Section: Minimum Necessary Data Rate For Estimation and Controlmentioning
confidence: 88%
“…Extensions of this result for stabilizing nonlinear systems in the vicinity of the origin and observing nonlinear systems through finite capacity communication channels, including large networks, were obtained in the series of the subsequent papers, see [58][59][60][61][62] to mention a few.…”
Section: Minimum Necessary Data Rate For Estimation and Controlmentioning
confidence: 88%
“…Extensions of this result for stabilizing nonlinear systems in the vicinity of the origin and observing nonlinear systems through finite capacity communication channels, including large networks, were obtained in the series of the subsequent papers, see [62][63][64][65][66] to mention a few.…”
Section: ηJ ηJ ηJmentioning
confidence: 88%
“…In the next result, h(f, K) stands for the entropy of the system (14) as defined in Section II-A for the system (1) of which ( 14) is a special case. (16) with a + ii as defined in Lemma 1.…”
Section: A Cascade Connectionmentioning
confidence: 99%
“…The entropy estimates that we derive involve upper bounds on the matrix measures and induced norms of the Jacobian matrices arising from the interconnection. (We note that entropy bounds of this form are very different from those obtained in [16].) For the case of a general interconnection, we establish an upper bound on the entropy that involves the largest eigenvalue of the interconnection matrix obtained from these Jacobians, multiplied by the system dimension.…”
mentioning
confidence: 91%
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