School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
Neural Netw. 2023 Jul;164:264-274. doi: 10.1016/j.neunet.2023.04.036. Epub 2023 Apr 28.
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real memristive neural networks. First, a real model of memristive neural networks is proposed by continuous differential equations, furthermore, it is simplified to neural networks with interval matrix uncertainties. Secondly, an aperiodic switching event-trigger is given, and the considered system switches between aperiodic sampled-data system and continuous event-triggered system. Thirdly, by constructing a time-dependent piecewise-defined Lyapunov functional, the stability criterion and the feedback gain design are obtained by linear matrix inequalities. Compared with the existing results, the stability criterion is with lower conservatism. Finally, two neurons are taken as examples to ensure the feasibility of the results.
针对时滞忆阻神经网络在非周期切换事件触发控制下的稳定化问题进行了研究。请注意,所研究的大多数时滞忆阻神经网络模型都是不连续的,这并不是真正的忆阻神经网络。首先,通过连续微分方程提出了忆阻神经网络的实际模型,然后将其简化为具有区间矩阵不确定性的神经网络。其次,给出了一种非周期切换事件触发机制,所考虑的系统在非周期采样系统和连续事件触发系统之间切换。第三,通过构造一个时变分段定义的李雅普诺夫函数,利用线性矩阵不等式得到了稳定性判据和反馈增益设计。与现有结果相比,该稳定性判据具有较低的保守性。最后,以两个神经元为例验证了结果的可行性。