MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Online Solution of Linear Time-Varying Matrix Equation AXB − C = 0

Different from gradient neural networks (GNN), a special kind of recurrent neural networks has been proposed recently by Zhang et al for solving online linear matrix equations with time-varying coefficients. Such recurrent neural networks, designed based on a matrix-valued error-function, could achieve global exponential convergence when solving online time-varying problems in comparison with gradient neural networks. This paper investigates the MATLAB simulation of Zhang neural networks (ZNN) for real-time solution of linear time-varying matrix equation AXB − C = 0. Gradient neural networks are simulated and compared as well. Simulation results substantiate the theoretical analysis and efficacy of ZNN on linear time-varying matrix equation solving.