Discrete-time Zhang neural network and numerical algorithm for time-varying quadratic minimization

A special class of recurrent neural network (RNN), termed Zhang neural network (ZNN), has been proposed, generalized and investigated for online solution of time-varying problems by following Zhang et al´s design method since 2001. For possible digital hardware realization, discrete-time ZNN models are proposed and investigated in this paper for time-varying quadratic minimization (QM). The proposed discrete-time ZNN models could utilize the time-derivative information of time-varying coefficients. For comparison, a discrete-time gradient neural network (GNN) model is also presented to solve the same time-varying QM problem. Simulative and numerical results demonstrate the efficacy and superiority of the proposed discrete-time ZNN models for time-varying QM, in comparison with the discrete-time GNN model.