Design of input assignment and feedback gain for re‐stabilizing undirected networks with High‐Dimension Low‐Sample‐Size data

There exists a critical transition before dramatic deterioration of complex dynamical system. Recently, method to predict such shifts based on High-Dimension Low-Sample-Size (HDLSS) data has been developed. Thus the prediction, it is important make system more stable by feedback control just transitions, which we call re-stabilization. However, re-stabilization cannot be achieved traditional stabilization methods as pole placement because available HDLSS not enough get mathematical model identification. In this article, model-free for proposed design optimal input assignment and gain undirected network systems only with data. The validated numerical simulations.