A Transfer Entropy Method to Quantify Causality in Stochastic Nonlinear Systems*. Issue 7 (2016)

• Record Type: Journal Article
• Authors: Gao, Jiaqi; Tulsyan, Aditya; Yang, Fan; Gopaluni, Bhushan
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 7(2016)
• Page Start: 454
• Page End: 459
• Publication Date: 2016
• Subjects: Causality -- transfer entropy -- particle filters -- state-space models; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.07.384
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.