English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper investigates the state estimation problem for a class of complexnetworks, in which the dynamics of each node is subject to Gaussian noise,system uncertainties and nonlinearities. Based on a regularized least-squaresapproach, the estimation problem is reformulated as an optimization problem,solving for a solution in a distributed way by utilizing a decouplingtechnique. Then, based on this solution, a class of estimators is designed tohandle the system dynamics and constraints. A novel feature of this design liesin the unified modeling of uncertainties and nonlinearities, the decoupling ofnodes, and the construction of recursive approximate covariance matrices forthe optimization problem. Furthermore, the feasibility of the proposedestimators and the boundedness of the mean-square errors are ensured under adeveloped criterion, which is easier to check than some typical estimationstrategies including the linear matrix inequalities-based and thevariance-constrained ones. Finally, the effectiveness of the theoreticalresults is verified by a numerical simulation.

A Distributed Optimization Scheme for State Estimation of Nonlinear Networks with Norm-bounded Uncertainties