中文摘要：

高阶Hopfield神经网络可以看作是Hopfield神经网络的扩展,相对一阶神经网络而言,高阶神经网络在存储能力、逼近能力、容错水平和收敛速度等方面具有更强大的能力.利用构造合适的Lyapunov泛函,应用不等式性质,研究了一类具有脉冲的高阶时滞Hopfield神经网络的动力学行为,得到了确保该系统的平衡点全局指数稳定的充分判别条件.通过两个仿真例子,说明所得结论的有效性.

英文摘要：

High-order Hopfield neural networks can be considered as an expansion of Hopfield neural networks,and have greater storage capacity,stronger approximation property,higher fault tolerance and faster convergence rate than lower-order neural networks.In this paper,dynamic behavior of high-order Hopfield neural networks with time delays and impulses are investigated.By employing appropriate Lyapunov functional and linear matrix inequality technique,several sufficient conditions are obtained for ensuring the system to be globally exponentially stable.Two illustrative examples are given to show the effectiveness of our results.