首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks
【24h】

A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks

机译:皮质网络控制区域的约束进化计算方法

获取原文
获取原文并翻译 | 示例

摘要

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regio- s in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.
机译:可以通过最小化扩展连接矩阵的本征比R和最大虚部sigma来检测用作关键节点的皮质网络中的控制区域,以将网络的动力学控制到所需状态。到目前为止,控制区域集的最佳选择仍然是一个悬而未决的问题,本文代表了将可控性的两种措施纳入一个统一框架的首次尝试。将皮质网络中控制区域的检测问题转换为约束优化问题(COP),其中目标函数R最小化,并且sigma被视为约束。然后,彻底研究了加权和定向复杂网络(例如猫的皮质网络)的控制区域的检测。皮质网络的控制区域已通过改进的动态混合框架(IDyHF)成功检测到。我们的实验证明,所提出的IDyHF在约束优化领域中的性能优于最近开发的两种进化计算方法,在控制理论和图论方面的性能优于某些传统方法。基于IDyHF,以微观和宏观的方式检测控制区域。我们的结果揭示了控制区域对驱动程序节点l的数量和约束r的依赖性。控制区域主要是从度数较大而度数较小的区域中选择的。当r = + infty时,驱动节点的平均度呈凹形,即,当l较小而度较小的区域为n时,度较大的区域对于网络的控制非常重要。当l增加时,有助于控制网络。当r = 0时,驱动程序节点的平均度随l增加。我们发现,随着l的增加,控制sigma在控制皮质网络中变得越来越重要。本文中控制区域的检测方法和结果将促进各种现实世界复杂网络的协调和信息共识,包括运输网络,遗传调控网络和社交网络等。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号