Sangam: A Confluence of Knowledge Streams

On controllability of neuronal networks with constraints on the average of control gains

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dc.creator Tang, Y
dc.creator Wang, Z
dc.creator Gao, H
dc.creator Qiao, H
dc.creator Kurths, J
dc.date 2015-01-16T12:50:23Z
dc.date 2014-12-01
dc.date 2015-01-16T12:50:23Z
dc.date 2014
dc.date.accessioned 2022-05-25T14:53:38Z
dc.date.available 2022-05-25T14:53:38Z
dc.identifier IEEE Transactions on Cybernetics, 44:12, pp. 2670 - 2681, 2014
dc.identifier 2168-2267
dc.identifier http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6787023
dc.identifier http://bura.brunel.ac.uk/handle/2438/9772
dc.identifier http://dx.doi.org/10.1109/TCYB.2014.2313154
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/172682
dc.description Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.
dc.format 2670 - 2681
dc.language eng
dc.language en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation IEEE Transactions on Cybernetics
dc.relation IEEE Transactions on Cybernetics
dc.subject Complex networks
dc.subject Controllability
dc.subject Evolutionary algorithms
dc.subject Multiagent systems
dc.subject Neural networks
dc.subject Synchronization/consensus
dc.title On controllability of neuronal networks with constraints on the average of control gains
dc.type Article


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