Zhang neural network solving for time-varying full-rank matrix Moore-Penrose inverse

Zhang neural networks (ZNN), a special kind of recurrent neural networks (RNN) with implicit dynamics, have recently been introduced to generalize to the solution of online time-varying problems. In comparison with conventional gradient-based neural networks, such RNN models are elegantly designed by defining matrix-valued indefinite error functions. In this paper, we generalize, investigate and analyze ZNN models for online time-varying full-rank matrix Moore-Penrose inversion. The computer-simulation results and application to inverse kinematic control of redundant robot arms demonstrate the feasibility and effectiveness of ZNN models for online time-varying full-rank matrix Moore-Penrose inversion.