High-dimensional time series are commonly encountered in modern control systems, especially in autonomous systems. In this letter, a novel parsimonious latent state space (LaSS) model is proposed to characterize the latent dynamics, achieving general latent dynamic modeling with dimension reduction. The LaSS model is optimized by alternating estimations of the dimension reduction projection and the latent state space model. Specifically, the latent state dynamics are estimated by stochastic subspace identification methods. Furthermore, the canonical correlation analysis (CCA) objective is employed to acquire the optimal predictability for the extracted latent variables. The proposed LaSS-CCA algorithm is tested on a real industrial case for its effectiveness.
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China under Grants 62075160, 61735011, and U2006216.
© 2017 IEEE.
- Dynamic factor models
- Dynamic latent variable models
- Latent state space models
- Reduced-dimensional dynamics