Stochastic recurent neural control for trajectory tracking of a gene regulatory network biological system

In this paper the problem of trajectory tracking by a stochastic recurrent neural network to a gene regulatory network described by a nonlinear dynamic model is studied. Based on the Lyapunov theory is obtained a control law of that achieves the global asymptotic stability of the tracking error.