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Research Article

Partial-nodes-based state estimation for linear complex networks with randomly occurring sensor delay and stochastic coupling strength

, ORCID Icon &
Pages 219-231 | Received 30 Dec 2020, Accepted 22 Feb 2021, Published online: 03 Mar 2021

Figures & data

Figure 1. The trajectories of x1,k and its estimations.

Figure 1. The trajectories of x1,k and its estimations.

Figure 2. The trajectories of x2,k and its estimations.

Figure 2. The trajectories of x2,k and its estimations.

Figure 3. The trajectories of x3,k and its estimations.

Figure 3. The trajectories of x3,k and its estimations.

Figure 4. The trajectories of x4,k and its estimations.

Figure 4. The trajectories of x4,k and its estimations.

Figure 5. The trajectories of x5,k and its estimations.

Figure 5. The trajectories of x5,k and its estimations.

Figure 6. log(MSE) with measurement outputs and corresponding upper bounds.

Figure 6. log(MSE) with measurement outputs and corresponding upper bounds.

Figure 7. log(MSE) without measurement outputs and corresponding upper bounds.

Figure 7. log(MSE) without measurement outputs and corresponding upper bounds.

Figure 8. tr(Σi,k|k) with measurement outputs under different probabilities (γ¯i,k).

Figure 8. tr(Σi,k|k) with measurement outputs under different probabilities (γ¯i,k).

Figure 9. tr(Σi,k|k) without measurement outputs under different probabilities (γ¯i,k).

Figure 9. tr(Σi,k|k) without measurement outputs under different probabilities (γ¯i,k).