Simulink Modeling and Comparison of Zhang Neural Networks and Gradient Neural Networks for Time-Varying Lyapunov Equation Solving

Abstract

In view of the great potential in parallel processing and ready implementation via hardware, neural networks are now often employed to solve online matrix algebraic problems. Recently, a special kind of recurrent neural network has been proposed by Zhang et al, which could be generalized to solving online Lyapunov equation with time-varying coefficient matrices. In comparison with gradient-based neural networks (GNN), the resultant Zhang neural networks (ZNN) perform much better on solving these time-varying problems. This paper investigates the MATLAB Simulink modeling, simulative verification and comparison of ZNN and GNN models for time-varying Lyapunov equation solving. Computer-simulation results verify that superior convergence and efficacy could be achieved by such ZNN models when solving the time-varying Lyapunov matrix equation, as compared to the GNN models.