A Noise-Tolerant Proximal Neurodynamic Algorithm for Solving MVIPs in Fixed-Time With Circuit Implementations and Applications

Abstract

In this paper, we propose a new noise-tolerant neurodynamic algorithm with fixed-time convergence to solve mixed variational inequality problems (MVIPs) and design the circuit framework for its hardware implementation. We prove that the proposed neurodynamic algorithm converges to a unique solution within fixed-time under some conditions and give its convergence time upper bound, which is independent of the initial states. Meanwhile, the robustness of the neurodynamic algorithm under additive perturbations is also demonstrated. In addition, the proposed neurodynamic algorithm is implemented using numerical simulation, analog circuits, and field-programmable gate array (FPGA) respectively. Finally, the superiority of the proposed algorithm is verified by two applications of image reconstruction and elastic net logistic regression.