Latent Dynamic Networked System Identification with High-Dimensional Networked Data
Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems. These systems produce high-dimensional data that reflect the complex interactions among the network nodes with rich sensor measurements. In this paper, we propose a novel algorithm for latent dynamic networked system identification that leverages the network structure and performs dimension reduction for each node via dynamic latent variables (DLVs). The algorithm assumes that the DLVs of each node have an auto-regressive model with exogenous input and interactions from other nodes. The DLVs of each node are extracted to capture the most predictable latent variables in the high dimensional data, while the residual factors are not predictable. The advantage of the proposed framework is demonstrated on an industrial process network for system identification and dynamic data analytics.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality ReductionSimilar Papers 제목 키워드 기반
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Estimating causal effects under networked interference is a crucial yet challenging problem. Existing methods based on observational data mainly rely on the networked unconfoundedness assumption, which guarantees the ide…
Representation LearningLINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contribute to the overall system behavior. Fo…
Time SeriesLearning Networked Dynamical System Models with Weak Form and Graph Neural Networks
This paper presents a sequence of two approaches for the data-driven control-oriented modeling of networked systems, i.e., the systems that involve many interacting dynamical components. First, a novel deep learning appr…
FormUnconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-…
Predicting Time Series of Networked Dynamical Systems without Knowing Topology
Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning the intrinsic dynamics from observational…
Time Series