A symbolic network-based nonlinear theory for dynamical systems observability.

ABSTRACT: When the state of the whole reaction network can be inferred by just measuring the dynamics of a limited set of nodes the system is said to be fully observable. However, as the number of all possible combinations of measured variables and time derivatives spanning the reconstructed state of the system exponentially increases with its dimension, the observability becomes a computationally prohibitive task. Our approach consists in computing the observability coefficients from a symbolic Jacobian matrix whose elements encode the linear, nonlinear polynomial or rational nature of the interaction among the variables. The novelty we introduce in this paper, required for treating large-dimensional systems, is to identify from the symbolic Jacobian matrix the minimal set of variables (together wi

SUBMITTER: Letellier C 

PROVIDER: S-EPMC5830642 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature