Recurrent implicit dynamics for online matrix inversion

A novel kind of recurrent implicit dynamics together with its electronic realization is proposed and exploited for real-time matrix inversion. Compared to conventional explicit neural dynamics, our proposed model in the form of implicit dynamics has the following advantages: (a) can coincide better with systems in practice; and (b) has higher abilities in representing dynamic systems. More importantly, our model can achieve superior convergence performance in comparison with the existing dynamic systems, specifically Gradient-based dynamics (GD) and recently-proposed Zhang dynamics (ZD). Theoretical analysis and computer simulation results substantiate the effectiveness and superior efficiency of our model for online matrix inversion.