I. Introduction
The online solution of nonlinear equations in the form of is considered to be a basic issue widely encountered in science and engineering fields. Many numerical algorithms have thus been presented and investigated [1]–[3]. However, for most numerical algorithms, they may not be efficient enough because of their serial-processing nature performed on digital computers [4]. A special class of recurrent neural network (termed Zhang neural network, ZNN) with implicit dynamics has been proposed by Zhang et al for solving online time-varying problems since March 2001 [5]–[8]. For example, by following the Zhang et al's design method, a ZNN-model [8] has recently been introduced to handle a scalar-valued nonlinear equation , In this paper, a new ZNN-model is developed and proposed to solve the systems of time-varying nonlinear equations in the form of , rather than the conventionally-investigated static nonlinear equation or time-varying nonlinear equation. To the best of the authors' knowledge, there is no literature specifically working on solving online systems of time-varying nonlinear equations at present stage. It can be viewed as one of the main motivations and contributions of this paper.