An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

Abstract

The field of position tracking control and communication engineering has been increasingly interested in time-varying quadratic minimization (TVQM). While traditional zeroing neural network (ZNN) models have been effective in solving TVQM problems, they have limitations in adapting their convergence rate to the commonly used convex activation function. To address this issue, we propose an adaptive non-convex activation zeroing neural network (AZNNNA) model in this paper. Using the Lyapunov theory, we theoretically analyze the global convergence and noise-immune characteristics of the proposed AZNNNA model under both noise-free and noise-perturbed scenarios. We also provide computer simulations to illustrate the effectiveness and superiority of the proposed model. Compared to existing ZNN models, our proposed AZNNNA model outperforms them in terms of efficiency, accuracy, and robustness. This has been demonstrated in the simulation experiment of this article.