Two Novel Noise-Suppression Projection Neural Networks With Fixed-Time Convergence for Variational Inequalities and Applications

This article proposes two novel projection neural networks (PNNs) with fixed-time (
$\mathbf{FIX_t}$
) convergence to deal with variational inequality problems (VIPs). The remarkable features of the proposed PNNs are
$\mathbf{FIX_t}$
convergence and more accurate upper bounds for arbitrary initial conditions. The robustness of the proposed PNNs under bounded noises is further studied. In addition, the proposed PNNs are applied to deal with absolute value equations (AVEs), noncooperative games, and sparse signal reconstruction problems (SSRPs). The upper bounds of the settling time for the proposed PNNs are tighter than the bounds in the existing neural networks. The effectiveness and advantages of the proposed PNNs are confirmed by numerical examples.