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A general recurrent neural network (RNN) with implicit dynamics has been proposed by Zhang et al for online time-varying algebraic equations solving; namely Zhang neural network (ZNN). In this type of network systems, neural dynamics are elegantly introduced by defining a matrix-valued error-monitoring function rather than the usual norm-based scalar-valued error funtion. This makes the computational error decrease to zero globally and exponentially. This paper investigates the modeling and simulation of ZNN using MATLAB Simulink and presents its convergence and robustness performance. MATLAB Simulink modeling results substantiate that this neural network is efficient for solving online linear time-varying equations.