Sparse network identifiability via compressed sensing

DOI10.1016/j.automatica.2016.01.008zbMath1334.93051OpenAlexW2284915482MaRDI QIDQ273889

Claire J. Tomlin, Young Hwan Chang, David Hayden, Jorge M. Gonçalves

Publication date: 22 April 2016

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://www.repository.cam.ac.uk/handle/1810/267296

zbMATH Keywords

closed-loop identificationlinear equationsdirected graphsidentifiabilityinterconnection matrices

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05) Applications of graph theory to circuits and networks (94C15)

Related Items

Secure estimation based Kalman filter for cyber-physical systems against sensor attacks ⋮ A microscopic-view infection model based on linear systems ⋮ Dynamic network reconstruction from heterogeneous datasets ⋮ Mining the hidden link structure from distribution flows for a spatial social network ⋮ A variational Bayesian approach to identifying whole-brain directed networks with fMRI data ⋮ Secure estimation against non‐fixed channel attacks in cyber‐physical systems ⋮ Prediction error identification of linear dynamic networks with rank-reduced noise ⋮ Identification of Dynamical Networks ⋮ Blind identification of fully observed linear time-varying systems via sparse recovery ⋮ Physics informed topology learning in networks of linear dynamical systems ⋮ Generic identifiability of subnetworks in a linear dynamic network: the full measurement case ⋮ Sparse estimation in linear dynamic networks using the stable spline horseshoe prior ⋮ Blind Identification of Stochastic Block Models from Dynamical Observations

Cites Work

• Unnamed Item
• Robust dynamical network structure reconstruction
• Identification of multi-input systems: variance analysis and input design issues
• A Bayesian approach to sparse dynamic network identification
• Model identification of a network as compressing sensing
• Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
• Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
• Causal Network Inference Via Group Sparse Regularization
• Compressed Sensing With Prior Information: Information-Theoretic Limits and Practical Decoders
• Necessary and Sufficient Conditions for Dynamical Structure Reconstruction of LTI Networks
• Sparsity and incoherence in compressive sampling
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Compressed sensing