Towards faster and robust solution for dynamic LR and QR factorization.

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND).