Future estimation algorithms

Other algorithms will be made available in a future release. To be included estimation algorithms are:

Full network identification for a given topology:

• S.J.M. Fonken, K.R. Ramaswamy and P.M.J. Van den Hof (2022). A scalable multi-step least squares method for network identification with unknown disturbance topology. Automatica, Volume 141 (110295), July 2022.

Topology estimation:

• S. Shi, G. Bottegal and P.M.J. Van den Hof (2019). Bayesian topology identification of linear dynamic networks. Proc. 2019 European Control Conference, Napels, Italy, June 25-28, 2019, pp. 2814-2819. Software is available through Codeocean.

Local module identification:

• V.R. Rajagopal, K.R. Ramaswamy and P.M.J. Van den Hof (2021). Learning local modules in dynamic networks without prior topology information. Proc. 60th IEEE Conf. Decision and Control, December 13-15, 2021, Austin, TX, USA, pp. 840-845.
• K.R. Ramaswamy, G. Bottegal and P.M.J. Van den Hof (2021). Learning linear models in a dynamic network using regularized kernel-based methods. Automatica, Vol. 129, Article 109591, July 2021.

Identification in diffusively coupled networks:

• E.M.M. Kivits and P.M.J. Van den Hof (2023). Identification of diffusively coupled linear networks through structured polynomial models. IEEE Trans. Automatic Control, June 2023.