A Transfer Entropy Method to Quantify Causality in Stochastic Nonlinear Systems

被引：4

作者：

Gao, Jiaqi ^{[1
]}

Tulsyan, Aditya ^{[2
]}

Yang, Fan ^{[3
]}

Gopaluni, Bhushan ^{[4
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] MIT, Dept Chem Engn, Cambridge, MA 02139 USA

[3] Tsinghua Univ, Dept Automat, Tsinghua Lab Informat Sci & Technol, Beijing 100084, Peoples R China

[4] Univ British Columbia, Dept Chem & Biol Engn, Vancouver, BC V6T 1Z3, Canada

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Causality; transfer entropy; particle filters; state-space models;

D O I：

10.1016/j.ifacol.2016.07.384

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In modern chemical processes, identification of the process variable connectivity and their topology is vital for maintaining the operational safety. As a general information theoretic method, transfer entropy can analyze the causality between two Variables based on estimation of conditional probability density functions. Transfer entropy estimation is typically a data driven method, however, the associated high computational complexity mid poor accuracy are not acceptable in real applications. Using a nonlinear stochastic state-space model in conjunction with particle filters, a novel transfer entropy estimation method is proposed. The proposed approach requires less data, is fast and accurate. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：454 / 459

页数：6

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