A recurrent neural network based on Taylor difference for solving discrete time-varying linear matrix problems and application in robot arms

Abstract

Discrete time-varying linear matrix problems (DTVLMP) play an important role in the field of artificial intelligence and control engineering. This article presents a direct solution to the DTVLMP based on Taylor difference discrete time-varying recurrent neural network (TD-DRNN) model. First of all, the TD-DRNN operates in a directly discrete time-varying framework, avoiding the need to build a theoretical foundation from a continuous time-varying recurrent neural network (RNN) model. Then, the theoretical properties of the TD-DRNN have been rigorously analyzed, demonstrating both its convergence and accuracy. These results show that the new TD-DRNN model has remarkable computational performance. Furthermore, the effectiveness and versatility of the TD-DRNN model have been substantiated through a numerical simulation and the application of two robotic trials.