2022/03/14 by I. Sendiña–Nadal, Sendiña-Nadal, Irene, Christophe Letellier +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Gene Regulatory Network Analysis #Neural Networks Stability and Synchronization #Nonlinear Dynamics and Pattern Formation
paper · pdf · doi:10.48550/arxiv.2203.07447
openalex publication_date 2022/03/14 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
We address the problem of retrieving the full state of a network of Rössler systems from the knowledge of the actual state of a limited set of nodes. The selection of the nodes where sensors are placed is carried out in a hierarchical way through a procedure based on graphical and symbolic observability approaches. By using a map directly obtained from the governing equations, we design a nonlinear network observer which is able to unfold the state of the non measured nodes with minimal error. For sparse networks, the number of sensors scales with half the network size and node reconstruction errors are lower in networks with heterogeneous degree distributions. The method performs well even in the presence of parameter mismatch and non-coherent dynamics and, therefore, we expect it to be useful for designing robust network control laws.