Prescribed-time cluster synchronization of coupled inertial neural networks: a lifting dimension approach

This paper focuses on the prescribed-time cluster synchronization of coupled inertial neural networks. By variable transformation, the coupled inertial neural networks are converted into high-order systems. An effective prescribed-time control applicable to high-order systems is introduced by virtue of a time-varying scaling function. Moreover, a dimension-lifting approach is utilized to derive sufficient criteria for achieving prescribed-time cluster synchronization under the proposed control. Compared with existing Lyapunov–Krasovskii functional methods, the criteria obtained in this paper are in form of low-dimensional linear matrix inequalities and therefore can be easily verified. A numerical example is provided to demonstrate the effectiveness of proposed results.