Abstract
In this paper, we propose a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective to estimate a fully-interacting reduced dimensional dynamic model. This algorithm is an advancement of the dynamic inner canonical correlation analysis (DiCCA) algorithm, which builds univariate latent autoregressive models that are non-interacting. The dynamic latent variable scores of the proposed algorithm are enforced to be orthogonal or contemporaneously independent, similar to those of DiCCA. An application case study on an industrial dataset is given to illustrate the superiority of the proposed algorithm. The reduced-dimensional latent dynamic model has potential applications for prediction, control, and diagnosis of systems with rich sensors, such as industrial internet of things.
Original language | English |
---|---|
Title of host publication | 2021 60th IEEE Conference on Decision and Control (CDC) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3689-3694 |
Number of pages | 6 |
ISBN (Electronic) | 9781665436595, 9781665436588 |
ISBN (Print) | 9781665436601 |
DOIs | |
Publication status | Published - Dec 2021 |
Externally published | Yes |
Event | 60th IEEE Conference on Decision and Control (CDC 2021) - Fairmont Hotel (Virtual), Austin, United States Duration: 13 Dec 2021 → 17 Dec 2021 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
---|---|
Volume | 2021-December |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 60th IEEE Conference on Decision and Control (CDC 2021) |
---|---|
Country/Territory | United States |
City | Austin |
Period | 13/12/21 → 17/12/21 |
Funding
Financial support for this work from the Natural Science Foundation of China grant, Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries (U20A201398), and the City University of Hong Kong under Project, SGP: Bridging between Systems Theory and Dynamic Data Learning towards Industrial Intelligence and Industry 4.0 (9380123), is gratefully acknowledged. The author acknowledges discussions and input from Dr. Jicong Fan of the Chinese University of Hong Kong, Shenzhen.