Varying-parameter finite-time zeroing neural network for solving linear algebraic systems

Abstract

A new recurrent neural network is presented for solving linear algebraic systems with finite-time convergence. The proposed model includes an exponential term in the Zhang neural network dynamical system, which leads to a faster convergence of the error-monitoring function in comparison to previous methods. Theoretical analysis, as well as simulation results, validate the efficacy of the proposed model.