Software

Developed software on dynamic network identification:

• A GUI-supported Matlab App/Toolbox SYSDYNET for structural analysis of dynamic networks and for data-driven modeling (identification) of full networks and subnetworks of prespecified topology.
  The app/toolbox handle both directed (Simulink-type) module networks, and non-directed (equation-based) diffusively coupled networks. Structural analysis involves, among others, analysis and allocation of sensors/actuators for guaranteeing network identifiability. It provides the user with tools to select measurable nodes, to assign a priori fixed/known modules, to analyse and synthesize full network or single module identifiability, and to construct and analyze predictor models for single module identification. A public Beta-version has been released in the early spring van 2023 and is available through the SYSDYNET Landing Page.

• Matlab tool for Bayesian topology identification of linear dynamic networks.
  Given a set of time series, the algorithm estimates the dynamic interconnection structure of the signals, which is equivalent to the estimation of Granger causality. The interaction of the signals is modeled by a network of transfer functions in the algorithm, which then employs a Bayesian criterion and a search algorithm to obtain an estimate of the network topology.
  Matlab software is available through CodeOcean. The method is documented in an ECC-2019 paper.