System Identification in the Network Era: A Survey of Data Issues and Innovative Approaches

Abstract

System identification is a data-driven modeling technique that originates from the control field. It constructs models from data to mimic the behavior of dynamic systems. However, in the network era, scenarios such as sensor malfunctions, packet loss, cyber-attacks, and big data affect the quality, integrity, and security of the data. These data issues pose significant challenges to traditional system identification methods. This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era. It explores cutting-edge methodologies to address data issues such as data loss, outliers, noise and nonlinear system identification for complex systems. To tackle the data loss, the methods based on imputation and likelihood-based inference (e.g., expectation maximization) have been employed. For outliers and noise, methods like robust regression (e.g., least median of squares, least trimmed squares) and low-rank matrix decomposition show progress in maintaining data integrity. Nonlinear system identification has advanced through kernel-based methods and neural networks, which can model complex data patterns. Finally, this paper provides valuable insights into potential directions for future research.