Robust Multiobjective Controllability of Complex Neuronal Networks.

Tang, Y; Gao, H; Du, W; Lu, J; Vasilakos, AV; Kurths, J

Robust Multiobjective Controllability of Complex Neuronal Networks.

Tang, Y Gao, H Du, W Lu, J Vasilakos, AV Kurths, J

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE/ACM Trans Comput Biol Bioinform, 2016, 13, (4), pp. 778-791
Issue Date:: 2016-07

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Field	Value	Language
dc.contributor.author	Tang, Y
dc.contributor.author	Gao, H
dc.contributor.author	Du, W
dc.contributor.author	Lu, J
dc.contributor.author	Vasilakos, AV
dc.contributor.author	Kurths, J
dc.date.accessioned	2022-08-10T04:48:17Z
dc.date.available	2022-08-10T04:48:17Z
dc.date.issued	2016-07
dc.identifier.citation	IEEE/ACM Trans Comput Biol Bioinform, 2016, 13, (4), pp. 778-791
dc.identifier.issn	1545-5963
dc.identifier.issn	1557-9964
dc.identifier.uri	http://hdl.handle.net/10453/159848
dc.description.abstract	This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE/ACM Trans Comput Biol Bioinform
dc.relation.isbasedon	10.1109/TCBB.2015.2485226
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Algorithms
dc.subject.mesh	Animals
dc.subject.mesh	Brain
dc.subject.mesh	Cats
dc.subject.mesh	Computational Biology
dc.subject.mesh	Cortical Synchronization
dc.subject.mesh	Models, Neurological
dc.subject.mesh	Models, Statistical
dc.subject.mesh	Nerve Net
dc.subject.mesh	Brain
dc.subject.mesh	Nerve Net
dc.subject.mesh	Animals
dc.subject.mesh	Cats
dc.subject.mesh	Cortical Synchronization
dc.subject.mesh	Models, Statistical
dc.subject.mesh	Computational Biology
dc.subject.mesh	Algorithms
dc.subject.mesh	Models, Neurological
dc.title	Robust Multiobjective Controllability of Complex Neuronal Networks.
dc.type	Journal Article
utslib.citation.volume	13
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-10T04:48:16Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	4

Abstract:

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

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http://hdl.handle.net/10453/159848