Periodically intermittent sampled-data control of a class of diffusion neural networks

In this paper, the stabilization problem of a class of diffusion neural networks via periodically intermittent sampleddata control is studied. It is assumed that the measurement of the states are taken in a finite number of fixed sampling points in the spatial domain and continuous in time. In the proposed control method, sampled-data control is only activated in “work time”, rather than during the whole time. A sufficient condition for the existence of periodically intermittent sampled-data controllers is derived in terms of linear matrix inequalities (LMIs). The obtained condition establishes a quantitative relation among the control period, the control width, and the upper bound on the spatial sampling intervals. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theoretical result.