An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis

Time-varying matrix inversion is an important field of research, and lots research achievements have been obtained. In the process solving time-varying inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while problem required. this paper, advanced continuous-time recurrent neural network (RNN) based on double integral RNN design formula proposed for continuous which has incomparable disturbance-suppression property. For digital hardware applications, corresponding discrete-time discretisation formulas. As result theoretical analysis, it demonstrated global exponential convergence performance, they are excellent suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments practical experiment, presented to demonstrate effectiveness superiority discrete with disturbance-suppression.