Maximum Likelihood Estimation for System Identification of Networks of Dynamical Systems

2025/06/25 by Anders Hansson, Hansson, Anders, João Victor Galvão da Mata +3 · 1 citation
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #Control Systems and Identification #FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.20628

openalex publication_date 2025/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This paper investigates maximum likelihood estimation for direct system identification in networks of dynamical systems. We establish that the proposed approach is both consistent and efficient. In addition, it is more generally applicable than existing methods, since it can be employed even when measurements are unavailable for all network nodes, provided that network identifiability is satisfied. Finally, we demonstrate that the maximum likelihood problem can be formulated without relying on a predictor, which is key to achieving computationally efficient numerical solutions.

Citations

• Single module identifiability in linear dynamic networks with partial excitation and measurement
• A local direct method for module identification in dynamic networks with\n correlated noise
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Signal selection for estimation and identification in networks of\n dynamic systems: a graphical model approach
• Identifiability of dynamical networks with partial node measurements
• A Reconstruction algorithm for an unknown network
• Topological identification in networks of dynamical systems

Cited by

• Maximum Likelihood Estimation of Dynamic Sub-Networks with Missing Data

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