文章基本信息

标题：A Transfer Entropy Method to Quantify Causality in Stochastic Nonlinear Systems
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作者：Jiaqi Gao ; Aditya Tulsyan ; Fan Yang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：7
页码：454-459
DOI：10.1016/j.ifacol.2016.07.384
语种：English
出版社：Elsevier
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 and 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.
关键词：Causalitytransfer entropyparticle filtersstate-space models