MATLAB Simulink modeling and simulation of Zhang neural networks for online time-varying sylvester equation solving

Recently, a special kind of recurrent neural networks has been proposed by Zhang et al for online solution of Sylvester equation with time-varying coefficients. Their neural dynamics are elegantly introduced by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function, so that the computational error can vanish to zero globally and exponentially. The resultant Zhang neural networks (ZNN), perform much better on solving time-varying problems in comparison with gradient-based neural networks. MATLAB Simulink is a software package for model-based design and multi-domain simulation of dynamic systems. By using click-and-drag mouse operations, it is much easier to model and simulate complex neural systems as compared to MATLAB coding. This paper investigates the MATLAB Simulink modeling and simulative verification of ZNN models for timevarying Sylvester equation solving. Computer-simulation results substantiate the ZNN efficacy on solving online the time-varying problems (specifically, the time-varying Sylvester equation).