Causality and Network Graph in General Bilinear State-Space Representations

被引：2

作者：

Jozsa, Monika ^{[1
,2
,3
]}

Petreczky, Mihaly ^{[4
]}

Camlibel, M. Kanat ^{[5
]}

机构：

[1] Univ Lille, Unit Rech Informat Automat, IMT Lille Douai, F-59000 Lille, France

[2] Univ Groningen, Bernoulli Inst Math Comp Sci & Artificial Intelli, NL-9700 AK Groningen, Netherlands

[3] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

[4] Ctr Rech Informat Signal & Automat Lille, F-59650 Villeneuve Dascq, France

[5] Univ Groningen, Johann Bernoulli Inst Math & Comp Sci & Artificia, NL-9700 AK Groningen, Netherlands

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2020年 / 65卷 / 08期

关键词：

Stochastic processes; Random variables; Algebra; Nonlinear systems; Linear systems; Biological system modeling; Interconnected systems; stochastic systems; system realization; BAYES NETS; REALIZATIONS; FEEDBACK;

D O I：

10.1109/TAC.2019.2952033

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 article proposes an extension of the well-known concept of Granger causality, called GB-Granger causality. GB-Granger causality is designed to relate the internal structure of bilinear state-space systems and statistical properties of their output processes. That is, if such a system generates two processes, where one does not GB-Granger cause the other, then it can be interpreted as the interconnection of two subsystems: one that sends information to the other, and one which does not send information back.This result is an extension of earlier obtained results on the relationship between Granger causality and the internal structure of linear time-invariant state-space representations.

引用

页码：3623 / 3630

页数：8

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