Controlling linear networks with minimally novel inputs

Gautam Kumar, Delsin Menolascino, Mohammadmehdi Kafashan, Shinung Ching

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


In this paper, we propose a novelty-based index for quantitative characterization of the controllability of complex networks. This inherently bounded index describes the average angular separation of an input with respect to the past input history. We use this index to find the minimally novel input that drives a linear network to a desired state using unit average energy. Specifically, the minimally novel input is defined as the solution of a continuous time, non-convex optimal control problem based on the introduced index. We provide conditions for existence and uniqueness, and an explicit, closed-form expression for the solution. We support our theoretical results by characterizing the minimally novel inputs for an example of a recurrent neuronal network.

Original languageEnglish
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2015 American Control Conference, ACC 2015
Country/TerritoryUnited States


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