Three nonlinearly-activated discrete-Time ZNN models for time-varying matrix inversion

Since March 2001, a special class of recurrent neural network termed Zhang neural network (ZNN) has been proposed by Zhang et al for time-varying matrix inversion. For the purpose of possible hardware implementation, the resultant ZNN model is discretized by employing Euler forward-difference rule. In this paper, three discrete-time ZNN models using nonlinear activation functions (e.g., power-sigmoid activation functions) are presented and investigated for time-varying matrix inversion. In addition, a criterion is proposed to measure the rapidity and accuracy of the presented discrete-time ZNN models for time-varying matrix inversion. Numerical results further demonstrate the efficacy of the presented discrete-time ZNN models for time-varying matrix inversion.