Adaptive finite-time cluster synchronization of neutral-type coupled neural networks with mixed delays - He, Juan-Juan; Lin, Ya-Qi; Ge, Ming-Feng; Liang, Chang-Duo; Ding, Teng-Fei; Wang, Leimin (Neurocomputing (Amsterdam), 2020)
This paper investigates the adaptive finite-time cluster synchronization problem of the neutral-type coupled neural networks (NCNNs) with mixed delays. Compared with the traditional neural networks, the model of NCNNs is more general in some sense, due to that it involves state delays, distributed delays and coupling delays. In this paper, a novel, adaptive and closed-loop control control algorithm is proposed to achieve the finite-time cluster synchronization of NCNNs with mixed delays. In addition, the sufficient conditions on the control parameters for stabilizing the closed-loop system are derived by leveraging the Lyapunov stability argument. Finally, the simulation results are carried out to illustrate the validity and feasibility of the proposed algorithm.