Controlling linear networks with minimally novel inputs

Gautam Kumar; Delsin Menolascino; Mohammadmehdi Kafashan; Shinung Ching

doi:10.1109/ACC.2015.7172264

Controlling linear networks with minimally novel inputs

Gautam Kumar, Delsin Menolascino, Mohammadmehdi Kafashan, Shinung Ching

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

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 language	English
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5896-5900
Number of pages	5
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7172264
State	Published - Jul 28 2015
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Conference

Conference	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	07/1/15 → 07/3/15

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10.1109/ACC.2015.7172264

Cite this

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title = "Controlling linear networks with minimally novel inputs",

abstract = "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.",

author = "Gautam Kumar and Delsin Menolascino and Mohammadmehdi Kafashan and Shinung Ching",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

year = "2015",

month = jul,

day = "28",

doi = "10.1109/ACC.2015.7172264",

language = "English",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "ACC 2015 - 2015 American Control Conference",

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Kumar, G, Menolascino, D, Kafashan, M & Ching, S 2015, Controlling linear networks with minimally novel inputs. in ACC 2015 - 2015 American Control Conference., 7172264, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 5896-5900, 2015 American Control Conference, ACC 2015, Chicago, United States, 07/1/15. https://doi.org/10.1109/ACC.2015.7172264

Controlling linear networks with minimally novel inputs. / Kumar, Gautam; Menolascino, Delsin; Kafashan, Mohammadmehdi et al.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5896-5900 7172264 (Proceedings of the American Control Conference; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Controlling linear networks with minimally novel inputs

AU - Kumar, Gautam

AU - Menolascino, Delsin

AU - Kafashan, Mohammadmehdi

AU - Ching, Shinung

PY - 2015/7/28

Y1 - 2015/7/28

N2 - 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.

AB - 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.

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DO - 10.1109/ACC.2015.7172264

M3 - Conference contribution

AN - SCOPUS:84940937800

T3 - Proceedings of the American Control Conference

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BT - ACC 2015 - 2015 American Control Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 American Control Conference, ACC 2015

Y2 - 1 July 2015 through 3 July 2015

ER -

Controlling linear networks with minimally novel inputs

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