Name
Abstract | ||
|---|---|---|
This paper studies the state estimation problem for complex networks with random coupling strength. Unlike the constant coupling strength used in the existing models, the coupling strength is assumed to be chosen from a uniform random distribution with non-negative mean. By employing the structure of the extended Kalman filter (EKF), a recursive state estimator is developed where the gain matrix is determined by optimizing an upper bound matrix despite the random coupling terms and linearization errors. Compared with the augmented approach for state estimation of complex networks, an important feature of the proposed estimator is that the gain matrix can be derived for each node separately. By using the stochastic analysis techniques, sufficient conditions are established to guarantee that the estimation error is bounded in mean square. Simulation results are provided to show the effectiveness and applicability of the proposed estimator. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.08.095 | Neurocomputing |
Keywords | Field | DocType |
State estimation,Complex networks,Extended Kalman filter,Random coupling strength | Applied mathematics,Coupling,Control theory,Matrix (mathematics),Upper and lower bounds,Artificial intelligence,Complex network,Linearization,Extended Kalman filter,Pattern recognition,Stochastic process,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
219 | C | 0925-2312 |
Citations | PageRank | References |
11 | 0.45 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenling Li | 1 | 227 | 18.83 |
| Yingmin Jia | 2 | 1743 | 135.37 |
| Junping Du | 3 | 789 | 91.80 |