A Complex-Valued Variant-Parameter Robust Zeroing Neural Network Model and its Applications

As an effective tool for solving dynamic complex value problems, especially dynamic complex value matrix inversion (DCVMI), the complex value zeroing neural network (CVZNN) has been widely applied in various practical industrial fields. However, previous complex value zeroing neural network (PCVZNN) models only achieve unsatisfactory exponential or finite time convergence, which usually does not conform to actual practical situation. Different from PCVZNN models, a complex-valued variant parameter robust ZNN model (CVVPRZNN) with both fixed-time convergence and robustness for solving DCVMI is proposed in this paper. The fixed-time convergence and noise suppression ability of the CVVPRZNN model for solving DCVMI are strictly deduced. Then, three comparative numerical experiments for DCVMI solving, dynamic circuit currents computing and manipulator trajectory tracking of the proposed CVVPRZNN model with other existing PCVZNN models are provided for demonstrating its better solution performances and higher practical application value. It should be noted that the experimental results indicate that regardless of whether it is disturbed by noise or not, the CVVPRZNN model only requires about 0.01 s to complete the corresponding solving task. However, in the same experimental environment, other models either cannot complete the solving task at all, or require about 8 s which is almost 800 times the solving time of the CVVPRZNN model to complete the corresponding dynamic problem solving.