Wan Ying, Cao Jinde, Wen Guanghui, Yu Wenwu
Department of Mathematics, Southeast University, Nanjing 210096, China.
Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Neural Netw. 2016 Jan;73:86-94. doi: 10.1016/j.neunet.2015.10.009. Epub 2015 Oct 26.
The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results.
研究了具有参数不确定性和时变延迟的Cohen-Grossberg神经网络的固定时间主从同步。与收敛时间依赖于初始同步误差的有限时间同步相比，固定时间同步的建立时间可以调整到期望值，而与初始条件无关。提出了一种用于从神经网络的新型同步控制策略。通过利用 Filippov 不连续理论和 Lyapunov 稳定性理论，提供了一些充分的方案来选择控制参数，以确保在存在参数不确定性的情况下与所需收敛时间同步。还推导了有限时间同步的调整控制输入的相应准则。最后，给出了两个数值例子来说明理论结果的有效性。
