Theory and Modern Applications
From: Variance-constrained resilient \(H_{\infty }\) state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements
k
\(K_{k}\)
1
\(K_{1}=[ -0.1701 \ 0.1102 ]^{T}\)
2
\(K_{2}=[ -0.1481 \ 0.2838 ]^{T}\)
3
\(K_{3}=[ -0.2438 \ 0.1024 ]^{T}\)
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