Introducing Robustness in Controllability of Neuronal Networks

被引：0
|
作者：
Tang Yang [1 ]
Du Wei [2 ]
机构：
[1] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China
[2] Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China
来源：
2015 34TH CHINESE CONTROL CONFERENCE (CCC) | 2015年
关键词：
Synchronization; Neuronal networks; Controllability; Robustness; Multiobjective optimization; ANATOMICAL CONNECTIVITY; COMPLEX NETWORKS; SYNCHRONIZATION; IDENTIFICATION; ORGANIZATION; SYSTEMS; CAT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 by including interval uncertainties is proposed for robust controllability. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affect the controllability of neuronal networks.
引用
收藏
页码：1309 / 1312
页数：4
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