A zeroing neural network for solving discrete time-varying minimization with different adjustable parameter

Abstract

It is popular approach to employ zeroing neural networks (ZNN) to handle time-varying problems. However, most of the existing methods have limited flexibility in the discretization process, which seriously affects the final effect of the model. To solve this problem, in this paper, we present a new discrete-time ZNN (DTZNN) for handling discrete time-varying minimization problem. In detail, the new DTZNN is proposed with four-instant general discretization formula, which has a adjustable parameter d . Generally speaking, in the process of discretization, different values of d and time interval can influence the residual error observably. In order to verify the effectiveness of our method, we conduct multiple numerical experiments. The numerical experiment results show that the new DTZNN has effectiveness and superiority for handling discrete time-varying minimization problem.