Dynamics and Numerics of Reciprocal-Kind Zhang Neural Network Solving Time-Dependent Linear Matrix-Vector Equation System

We propose a novel neural-dynamic approach called reciprocal-kind Zhang neural network (RKZNN) for solving time-dependent linear matrix-vector equation system (TD-LMVES). The model of the approach is depicted in an explicit dynamic equation, which can be simulated by numerical algorithms for ordinary derivative equations. Computer simulations are performed to verify the effectiveness of the proposed approach. The results illustrate that the numerical solutions computed via RKZNN converge to the theoretical ones, and the convergence rate can be adjusted by changing the value of convergence parameter in the dynamic equation.