https://www.selleckchem.com/pr....oducts/pirfenidone.h
This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the H∞ state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are repres
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