Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Science Program, Texas A&M University at Qatar, Doha 23874, Qatar.
Neural Netw. 2023 May;162:288-296. doi: 10.1016/j.neunet.2023.03.002. Epub 2023 Mar 8.
This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.
本文研究了具有不确定性的离散时间主从神经网络（MSNNs）的有界同步。为了处理 MSNNs 中的未知参数，提出了一种参数自适应律，并结合脉冲机制来提高估计效率。同时，脉冲方法也应用于控制器设计，以节省能源。此外，采用了一种新的时变 Lyapunov 泛函候选来描述 MSNNs 的脉冲动态特性，其中使用了一个与脉冲间隔相关的凸函数来获得 MSNNs 有界同步的充分条件。基于该条件，利用单位矩阵计算控制器增益。提出了一种算法来通过优化其参数来减小同步误差的边界。最后，通过一个数值例子验证了所提出的结果的正确性和优越性。
