Different complex ZFs leading to different complex ZNN models for time-varying complex matrix inversion

The Zhang neural network (ZNN), as a special class of recurrent neural network (RNN), has been proposed by Zhang et al. for the online solution of various time-varying problems. More importantly, such a ZNN is based on the Zhang function (ZF) as the error-monitoring function, which is indefinite and quite different from the usual error functions in the study of conventional algorithms, such as a scalar-valued norm-based energy function involved in the gradient-based neural network (GNN). Meanwhile, the resultant ZNN model can guarantee the global/exponential convergence performance for online time-varying problems solving by following Zhang et al.´s design method. In this paper, focusing on solving the time-varying complex matrix-inversion problem, the complex ZNN models are proposed, developed and investigated for time-varying complex matrix inversion. In addition, by introducing different complex ZFs, different corresponding complex ZNN models can be proposed and developed for time-varying complex matrix inversion. Finally, through some simulations and verifications, the illustrative results substantiate the efficacy of the complex ZNN models based on different complex ZFs for time-varying complex matrix inversion.