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Superior Performance of Using Hyperbolic Sine Activation Functions in ZNN Illustrated via Time-Varying Matrix Square Roots Finding

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Figure

Fig. 1. Activation function f(·) used in the neural network.
Fig. 2. Online solution of time-varying matrix square root problem (9) by ZNN (4) withthe theoretical time-varying square rootγ = 1, where the neural-network solutions X(t) are denoted by solid blue curves, and A1/2(t) is denoted by red dash-dotted curves.
Fig. 3. Residual errors of ZNN (4) with γ = 1 for TVMSR finding of (9).
Fig. 4. Residual errors of largely-perturbed ZNN (8) for time-varying matrix square rootsfinding of (9) (with γ = 1 and ∆ωij = 102, i, j ∈ {1, 2}).
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