On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	On Controllability of Neuronal Networks With Constraints on the Average of Control Gains
	Tang, Yang 1,2; Wang, Zidong 3,4; Gao, Huijun 5,6; Qiao, Hong7 ; Kurths, Juergen 1,2,8
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2014-12-01
卷号	44 期号:12 页码:2670-2681
关键词	Complex networks controllability evolutionary algorithms multiagent systems neural networks synchronization/consensus
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
研究领域[WOS]	Computer Science
关键词[WOS]	BRAIN NETWORKS ; EVOLUTIONARY ALGORITHMS ; COMPLEX NETWORKS ; SYNCHRONIZATION ; OPTIMIZATION ; ORGANIZATION ; CAT ; IDENTIFICATION ; CONNECTIVITY ; SYSTEMS
收录类别	SCI
语种	英语
WOS记录号	WOS:000345629000035
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3027]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位	1.Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany 2.Humboldt Univ, Inst Phys, D-12489 Berlin, Germany 3.Brunel Univ, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England 4.King Abdulaziz Univ, Fac Engn, Jeddah 21589, Saudi Arabia 5.Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150080, Peoples R China 6.King Abdulaziz Univ, Jeddah 21589, Saudi Arabia 7.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 8.Univ Aberdeen, Inst Complex Syst & Math Biol, Aberdeen AB24 3UE, Scotland
推荐引用方式 GB/T 7714	Tang, Yang,Wang, Zidong,Gao, Huijun,et al. On Controllability of Neuronal Networks With Constraints on the Average of Control Gains[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(12):2670-2681.
APA	Tang, Yang,Wang, Zidong,Gao, Huijun,Qiao, Hong,&Kurths, Juergen.(2014).On Controllability of Neuronal Networks With Constraints on the Average of Control Gains.IEEE TRANSACTIONS ON CYBERNETICS,44(12),2670-2681.
MLA	Tang, Yang,et al."On Controllability of Neuronal Networks With Constraints on the Average of Control Gains".IEEE TRANSACTIONS ON CYBERNETICS 44.12(2014):2670-2681.