论文标题
通过新加权积分不等式的时间延迟神经网络的指数稳定性
Exponential stability for time-delay neural networks via new weighted integral inequalities
论文作者
论文摘要
我们研究了具有时间变化延迟的神经网络的指数稳定性。通过扩展基于辅助函数的积分不等式，通过使用加权正交函数来得出一种新颖的积分不等式，其中一个函数是不连续的。然后，使用新的不等式通过Lyapunov-Krasovskii功能（LKF）方法研究时间延迟神经网络的指数稳定性。给出了数值示例以验证所提出的标准的优势。
We study exponential stability for a kind of neural networks having time-varying delay. By extending the auxiliary function-based integral inequality, a novel integral inequality is derived by using weighted orthogonal functions of which one is discontinuous. Then, the new inequality is applied to investigate the exponential stability of time-delay neural networks via Lyapunov-Krasovskii functional (LKF) method. Numerical examples are given to verify the advantages of the proposed criterion.
